Processing row data via a plurality of processing core resources

ABSTRACT

A record processing and storage system is operable to receive a plurality of row data that each indicate a batch number. Each of the plurality of row data is added to a pending row data pool. A plurality of pages is generated from the plurality of row data via a plurality of processing core resources. Each processing core resource in the plurality of processing core resources processing a corresponding subset of the plurality of row data by retrieving, in each time slice of a plurality of time slices, one row data from the pending row data pool with a most favorably ordered batch number of row data in the pending row data pool. Each processing core resource further processes the one row data in the in each time slice to participate in generation of at least one of the plurality of pages.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.16/985,930, entitled “RECORD DEDUPLICATION IN DATABASE SYSTEMS”, filedAug. 5, 2020, which is hereby incorporated herein by reference in itsentirety and made part of the present U.S. Utility patent applicationfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networking and moreparticularly to database system and operation.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction.

Of the many applications a computer can perform, a database system isone of the largest and most complex applications. In general, a databasesystem stores a large amount of data in a particular way for subsequentprocessing. In some situations, the hardware of the computer is alimiting factor regarding the speed at which a database system canprocess a particular function. In some other instances, the way in whichthe data is stored is a limiting factor regarding the speed ofexecution. In yet some other instances, restricted co-process optionsare a limiting factor regarding the speed of execution.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a large scaledata processing network that includes a database system in accordancewith the present invention;

FIG. 1A is a schematic block diagram of an embodiment of a databasesystem in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of anadministrative sub-system in accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of a configurationsub-system in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of a parallelizeddata input sub-system in accordance with the present invention;

FIG. 5 is a schematic block diagram of an embodiment of a parallelizedquery and response (Q&R) sub-system in accordance with the presentinvention;

FIG. 6 is a schematic block diagram of an embodiment of a parallelizeddata store, retrieve, and/or process (IO& P) sub-system in accordancewith the present invention;

FIG. 7 is a schematic block diagram of an embodiment of a computingdevice in accordance with the present invention;

FIG. 8 is a schematic block diagram of another embodiment of a computingdevice in accordance with the present invention;

FIG. 9 is a schematic block diagram of another embodiment of a computingdevice in accordance with the present invention;

FIG. 10 is a schematic block diagram of an embodiment of a node of acomputing device in accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a node of acomputing device in accordance with the present invention;

FIG. 12 is a schematic block diagram of an embodiment of a node of acomputing device in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of a node of acomputing device in accordance with the present invention;

FIG. 14 is a schematic block diagram of an embodiment of operatingsystems of a computing device in accordance with the present invention;

FIGS. 15-23 are schematic block diagrams of an example of processing atable or data set for storage in the database system in accordance withthe present invention;

FIG. 24A is a schematic block diagram of a query execution planimplemented via a plurality of nodes in accordance with variousembodiments of the present invention;

FIGS. 24B-24D are schematic block diagrams of embodiments of a node thatimplements a query processing module in accordance with variousembodiments of the present invention;

FIGS. 25A-25B are schematic block diagrams of embodiments of a databasesystem that includes a record processing and storage system inaccordance with various embodiments of the present invention;

FIG. 25C is a is a schematic block diagrams of an embodiment of a pagegenerator in accordance with various embodiments of the presentinvention;

FIG. 25D is a schematic block diagrams of an embodiment of a pagestorage system of a record processing and storage system in accordancewith various embodiments of the present invention;

FIG. 25E is a schematic block diagrams of a node that implements a queryprocessing module that reads records from segment storage and pagestorage in accordance with various embodiments of the present invention;

FIG. 26A is a schematic block diagram of a segment generator of a recordprocessing and storage system in accordance with various embodiments ofthe present invention;

FIG. 26B is a schematic block diagram illustrating operation of a pageconversion determination module over time in accordance with variousembodiments of the present invention;

FIG. 26C is a schematic block diagram of a cluster key-based groupingmodule of a segment generator in accordance with various embodiments ofthe present invention;

FIG. 27A is a schematic block diagram illustrating communication betweena record processing and storage system and a data source in accordancewith various embodiments of the present invention;

FIGS. 27B-27D are schematic block diagrams that illustrate exampleembodiments of labeled row data in accordance with various embodimentsof the present invention;

FIG. 27E is a schematic block diagram illustrating communication betweena record processing and storage system and a plurality of data sourcesin accordance with various embodiments of the present invention;

FIGS. 27F-27H are schematic block diagrams illustrating a data sourcethat maintains a confirmation-pending row list in accordance withvarious embodiments of the present invention;

FIG. 27I is a logic diagram illustrating a method of generating pages inaccordance with various embodiments of the present invention;

FIG. 27J is a logic diagram illustrating a method of transmittinglabeled row data based on a confirmation-pending row list in accordancewith various embodiments of the present invention;

FIG. 28A is a schematic block diagram of an embodiment of a recordprocessing and storage system that generates page metadata from labeledrow data in accordance with various embodiments of the presentinvention;

FIG. 28B is a schematic block diagram of an embodiment of a recordprocessing and storage system that implements a row deduplication modulein accordance with various embodiments of the present invention;

FIG. 28C is an illustration depicting the page deduplication based onpage metadata performed by a row deduplication module in accordance withvarious embodiments of the present invention;

FIG. 28D is an illustration of a record processing and storage systemthat performs page deduplication via communication between a pluralityof nodes in accordance with various embodiments of the presentinvention;

FIG. 28E is a logic diagram illustrating a method of deduplicating pagesin accordance with various embodiments of the present invention;

FIG. 29A is a schematic block diagram of an embodiment of a rowdeduplication module that implements a page set filtering module inaccordance with various embodiments of the present invention;

FIG. 29B is a schematic block diagram of an example embodiment apotential intersection detection function implemented by a page setfiltering module in accordance with various embodiments of the presentinvention;

FIG. 29C is a logic diagram illustrating a method of deduplicating pagesin accordance with various embodiments of the present invention;

FIG. 30A is a schematic block diagram of an embodiment of a recordprocessing and storage system that performs separate page deduplicationfor each of a plurality of storage clusters in accordance with variousembodiments of the present invention;

FIG. 30B is a schematic block diagram of an embodiment of a recordprocessing and storage system that implements a data source assignmentmodule in accordance with various embodiments of the present invention;

FIG. 30C is a schematic block diagram of an embodiment of a recordprocessing and storage system that receives labeled row data from aplurality of data sources;

FIG. 30D is a logic diagram illustrating a method of deduplicating pagesin accordance with various embodiments of the present invention;

FIG. 31A is a schematic block diagram of an embodiment of a pagegenerator that utilizes a plurality of processing core resources togenerate pages in accordance with various embodiments of the presentinvention;

FIGS. 31B-31E are schematic block diagrams illustrating an exampleembodiment of generating pages via a plurality of processing coreresources over time in accordance with various embodiments of thepresent invention;

FIG. 31F is a logic diagram illustrating a method of generating pages inaccordance with various embodiments of the present invention;

FIG. 32A is a schematic block diagram of an embodiment of a pagegenerator that implements durability data generator module tocommunicate row durability data to a data source in accordance withvarious embodiments of the present invention;

FIG. 32B is a schematic block diagram of an embodiment of a durabilitydata generator module in accordance with various embodiments of thepresent invention; and

FIG. 32C is a logic diagram illustrating a method of generating rowdurability data in accordance with various embodiments of the presentinvention;

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a large-scaledata processing network that includes data gathering devices (1, 1-1through 1-n), data systems (2, 2-1 through 2-N), data storage systems(3, 3-1 through 3-n), a network 4, and a database system 10. The datagathering devices are computing devices that collect a wide variety ofdata and may further include sensors, monitors, measuring instruments,and/or other instrument for collecting data. The data gathering devicescollect data in real-time (i.e., as it is happening) and provides it todata system 2-1 for storage and real-time processing of queries 5-1 toproduce responses 6-1. As an example, the data gathering devices arecomputing in a factory collecting data regarding manufacturing of one ormore products and the data system is evaluating queries to determinemanufacturing efficiency, quality control, and/or product developmentstatus.

The data storage systems 3 store existing data. The existing data mayoriginate from the data gathering devices or other sources, but the datais not real time data. For example, the data storage system storesfinancial data of a bank, a credit card company, or like financialinstitution. The data system 2-N processes queries 5-N regarding thedata stored in the data storage systems to produce responses 6-N.

Data system 2 processes queries regarding real time data from datagathering devices and/or queries regarding non-real time data stored inthe data storage system 3. The data system 2 produces responses inregard to the queries. Storage of real time and non-real time data, theprocessing of queries, and the generating of responses will be discussedwith reference to one or more of the subsequent figures.

FIG. 1A is a schematic block diagram of an embodiment of a databasesystem 10 that includes a parallelized data input sub-system 11, aparallelized data store, retrieve, and/or process sub-system 12, aparallelized query and response sub-system 13, system communicationresources 14, an administrative sub-system 15, and a configurationsub-system 16. The system communication resources 14 include one or moreof wide area network (WAN) connections, local area network (LAN)connections, wireless connections, wireline connections, etc. to couplethe sub-systems 11, 12, 13, 15, and 16 together.

Each of the sub-systems 11, 12, 13, 15, and 16 include a plurality ofcomputing devices; an example of which is discussed with reference toone or more of FIGS. 7-9 . Hereafter, the parallelized data inputsub-system 11 may also be referred to as a data input sub-system, theparallelized data store, retrieve, and/or process sub-system may also bereferred to as a data storage and processing sub-system, and theparallelized query and response sub-system 13 may also be referred to asa query and results sub-system.

In an example of operation, the parallelized data input sub-system 11receives a data set (e.g., a table) that includes a plurality ofrecords. A record includes a plurality of data fields. As a specificexample, the data set includes tables of data from a data source. Forexample, a data source includes one or more computers. As anotherexample, the data source is a plurality of machines. As yet anotherexample, the data source is a plurality of data mining algorithmsoperating on one or more computers.

As is further discussed with reference to FIG. 15 , the data sourceorganizes its records of the data set into a table that includes rowsand columns. The columns represent data fields of data for the rows.Each row corresponds to a record of data. For example, a table includepayroll information for a company's employees. Each row is an employee'spayroll record. The columns include data fields for employee name,address, department, annual salary, tax deduction information, directdeposit information, etc.

The parallelized data input sub-system 11 processes a table to determinehow to store it. For example, the parallelized data input sub-system 11divides the data set into a plurality of data partitions. For eachpartition, the parallelized data input sub-system 11 divides it into aplurality of data segments based on a segmenting factor. The segmentingfactor includes a variety of approaches divide a partition intosegments. For example, the segment factor indicates a number of recordsto include in a segment. As another example, the segmenting factorindicates a number of segments to include in a segment group. As anotherexample, the segmenting factor identifies how to segment a datapartition based on storage capabilities of the data store and processingsub-system. As a further example, the segmenting factor indicates howmany segments for a data partition based on a redundancy storageencoding scheme.

As an example of dividing a data partition into segments based on aredundancy storage encoding scheme, assume that it includes a 4 of 5encoding scheme (meaning any 4 of 5 encoded data elements can be used torecover the data). Based on these parameters, the parallelized datainput sub-system 11 divides a data partition into 5 segments: onecorresponding to each of the data elements).

The parallelized data input sub-system 11 restructures the plurality ofdata segments to produce restructured data segments. For example, theparallelized data input sub-system 11 restructures records of a firstdata segment of the plurality of data segments based on a key field ofthe plurality of data fields to produce a first restructured datasegment. The key field is common to the plurality of records. As aspecific example, the parallelized data input sub-system 11 restructuresa first data segment by dividing the first data segment into a pluralityof data slabs (e.g., columns of a segment of a partition of a table).Using one or more of the columns as a key, or keys, the parallelizeddata input sub-system 11 sorts the data slabs. The restructuring toproduce the data slabs is discussed in greater detail with reference toFIG. 4 and FIGS. 16-18 .

The parallelized data input sub-system 11 also generates storageinstructions regarding how sub-system 12 is to store the restructureddata segments for efficient processing of subsequently received queriesregarding the stored data. For example, the storage instructions includeone or more of: a naming scheme, a request to store, a memory resourcerequirement, a processing resource requirement, an expected accessfrequency level, an expected storage duration, a required maximum accesslatency time, and other requirements associated with storage,processing, and retrieval of data.

A designated computing device of the parallelized data store, retrieve,and/or process sub-system 12 receives the restructured data segments andthe storage instructions. The designated computing device (which israndomly selected, selected in a round robin manner, or by default)interprets the storage instructions to identify resources (e.g., itself,its components, other computing devices, and/or components thereof)within the computing device's storage cluster. The designated computingdevice then divides the restructured data segments of a segment group ofa partition of a table into segment divisions based on the identifiedresources and/or the storage instructions. The designated computingdevice then sends the segment divisions to the identified resources forstorage and subsequent processing in accordance with a query. Theoperation of the parallelized data store, retrieve, and/or processsub-system 12 is discussed in greater detail with reference to FIG. 6 .

The parallelized query and response sub-system 13 receives queriesregarding tables (e.g., data sets) and processes the queries prior tosending them to the parallelized data store, retrieve, and/or processsub-system 12 for execution. For example, the parallelized query andresponse sub-system 13 generates an initial query plan based on a dataprocessing request (e.g., a query) regarding a data set (e.g., thetables). Sub-system 13 optimizes the initial query plan based on one ormore of the storage instructions, the engaged resources, andoptimization functions to produce an optimized query plan.

For example, the parallelized query and response sub-system 13 receivesa specific query no. 1 regarding the data set no. 1 (e.g., a specifictable). The query is in a standard query format such as Open DatabaseConnectivity (ODBC), Java Database Connectivity (JDBC), and/or SPARK.The query is assigned to a node within the parallelized query andresponse sub-system 13 for processing. The assigned node identifies therelevant table, determines where and how it is stored, and determinesavailable nodes within the parallelized data store, retrieve, and/orprocess sub-system 12 for processing the query.

In addition, the assigned node parses the query to create an abstractsyntax tree. As a specific example, the assigned node converts an SQL(Standard Query Language) statement into a database instruction set. Theassigned node then validates the abstract syntax tree. If not valid, theassigned node generates a SQL exception, determines an appropriatecorrection, and repeats. When the abstract syntax tree is validated, theassigned node then creates an annotated abstract syntax tree. Theannotated abstract syntax tree includes the verified abstract syntaxtree plus annotations regarding column names, data type(s), dataaggregation or not, correlation or not, sub-query or not, and so on.

The assigned node then creates an initial query plan from the annotatedabstract syntax tree. The assigned node optimizes the initial query planusing a cost analysis function (e.g., processing time, processingresources, etc.) and/or other optimization functions. Having producedthe optimized query plan, the parallelized query and response sub-system13 sends the optimized query plan to the parallelized data store,retrieve, and/or process sub-system 12 for execution. The operation ofthe parallelized query and response sub-system 13 is discussed ingreater detail with reference to FIG. 5 .

The parallelized data store, retrieve, and/or process sub-system 12executes the optimized query plan to produce resultants and sends theresultants to the parallelized query and response sub-system 13. Withinthe parallelized data store, retrieve, and/or process sub-system 12, acomputing device is designated as a primary device for the query plan(e.g., optimized query plan) and receives it. The primary deviceprocesses the query plan to identify nodes within the parallelized datastore, retrieve, and/or process sub-system 12 for processing the queryplan. The primary device then sends appropriate portions of the queryplan to the identified nodes for execution. The primary device receivesresponses from the identified nodes and processes them in accordancewith the query plan.

The primary device of the parallelized data store, retrieve, and/orprocess sub-system 12 provides the resulting response (e.g., resultants)to the assigned node of the parallelized query and response sub-system13. For example, the assigned node determines whether further processingis needed on the resulting response (e.g., joining, filtering, etc.). Ifnot, the assigned node outputs the resulting response as the response tothe query (e.g., a response for query no. 1 regarding data set no. 1).If, however, further processing is determined, the assigned node furtherprocesses the resulting response to produce the response to the query.Having received the resultants, the parallelized query and responsesub-system 13 creates a response from the resultants for the dataprocessing request.

FIG. 2 is a schematic block diagram of an embodiment of theadministrative sub-system 15 of FIG. 1A that includes one or morecomputing devices 18-1 through 18-n. Each of the computing devicesexecutes an administrative processing function utilizing a correspondingadministrative processing of administrative processing 19-1 through 19-n(which includes a plurality of administrative operations) thatcoordinates system level operations of the database system. Eachcomputing device is coupled to an external network 17, or networks, andto the system communication resources 14 of FIG. 1A.

As will be described in greater detail with reference to one or moresubsequent figures, a computing device includes a plurality of nodes andeach node includes a plurality of processing core resources. Eachprocessing core resource is capable of executing at least a portion ofan administrative operation independently. This supports lock free andparallel execution of one or more administrative operations.

The administrative sub-system 15 functions to store metadata of the dataset described with reference to FIG. 1A. For example, the storingincludes generating the metadata to include one or more of an identifierof a stored table, the size of the stored table (e.g., bytes, number ofcolumns, number of rows, etc.), labels for key fields of data segments,a data type indicator, the data owner, access permissions, availablestorage resources, storage resource specifications, software foroperating the data processing, historical storage information, storagestatistics, stored data access statistics (e.g., frequency, time of day,accessing entity identifiers, etc.) and any other information associatedwith optimizing operation of the database system 10.

FIG. 3 is a schematic block diagram of an embodiment of theconfiguration sub-system 16 of FIG. 1A that includes one or morecomputing devices 18-1 through 18-n. Each of the computing devicesexecutes a configuration processing function 20-1 through 20-n (whichincludes a plurality of configuration operations) that coordinatessystem level configurations of the database system. Each computingdevice is coupled to the external network 17 of FIG. 2 , or networks,and to the system communication resources 14 of FIG. 1A.

FIG. 4 is a schematic block diagram of an embodiment of the parallelizeddata input sub-system 11 of FIG. 1A that includes a bulk data sub-system23 and a parallelized ingress sub-system 24. The bulk data sub-system 23includes a plurality of computing devices 18-1 through 18-n. A computingdevice includes a bulk data processing function (e.g., 27-1) forreceiving a table from a network storage system 21 (e.g., a server, acloud storage service, etc.) and processing it for storage as generallydiscussed with reference to FIG. 1A.

The parallelized ingress sub-system 24 includes a plurality of ingressdata sub-systems 25-1 through 25-p that each include a localcommunication resource of local communication resources 26-1 through26-p and a plurality of computing devices 18-1 through 18-n. A computingdevice executes an ingress data processing function (e.g., 28-1) toreceive streaming data regarding a table via a wide area network 22 andprocessing it for storage as generally discussed with reference to FIG.1A. With a plurality of ingress data sub-systems 25-1 through 25-p, datafrom a plurality of tables can be streamed into the database system 10at one time.

In general, the bulk data processing function is geared towardsreceiving data of a table in a bulk fashion (e.g., the table exists andis being retrieved as a whole, or portion thereof). The ingress dataprocessing function is geared towards receiving streaming data from oneor more data sources (e.g., receive data of a table as the data is beinggenerated). For example, the ingress data processing function is gearedtowards receiving data from a plurality of machines in a factory in aperiodic or continual manner as the machines create the data.

FIG. 5 is a schematic block diagram of an embodiment of a parallelizedquery and results sub-system 13 that includes a plurality of computingdevices 18-1 through 18-n. Each of the computing devices executes aquery (Q) & response (R) processing function 33-1 through 33-n. Thecomputing devices are coupled to the wide area network 22 to receivequeries (e.g., query no. 1 regarding data set no. 1) regarding tablesand to provide responses to the queries (e.g., response for query no. 1regarding the data set no. 1). For example, a computing device (e.g.,18-1) receives a query, creates an initial query plan therefrom, andoptimizes it to produce an optimized plan. The computing device thensends components (e.g., one or more operations) of the optimized plan tothe parallelized data store, retrieve, &/or process sub-system 12.

Processing resources of the parallelized data store, retrieve, &/orprocess sub-system 12 processes the components of the optimized plan toproduce results components 32-1 through 32-n. The computing device ofthe Q&R sub-system 13 processes the result components to produce a queryresponse.

The Q&R sub-system 13 allows for multiple queries regarding one or moretables to be processed concurrently. For example, a set of processingcore resources of a computing device (e.g., one or more processing coreresources) processes a first query and a second set of processing coreresources of the computing device (or a different computing device)processes a second query.

As will be described in greater detail with reference to one or moresubsequent figures, a computing device includes a plurality of nodes andeach node includes multiple processing core resources such that aplurality of computing devices includes pluralities of multipleprocessing core resources A processing core resource of the pluralitiesof multiple processing core resources generates the optimized query planand other processing core resources of the pluralities of multipleprocessing core resources generates other optimized query plans forother data processing requests. Each processing core resource is capableof executing at least a portion of the Q & R function. In an embodiment,a plurality of processing core resources of one or more nodes executesthe Q & R function to produce a response to a query. The processing coreresource is discussed in greater detail with reference to FIG. 13 .

FIG. 6 is a schematic block diagram of an embodiment of a parallelizeddata store, retrieve, and/or process sub-system 12 that includes aplurality of computing devices, where each computing device includes aplurality of nodes and each node includes multiple processing coreresources. Each processing core resource is capable of executing atleast a portion of the function of the parallelized data store,retrieve, and/or process sub-system 12. The plurality of computingdevices is arranged into a plurality of storage clusters. Each storagecluster includes a number of computing devices.

In an embodiment, the parallelized data store, retrieve, and/or processsub-system 12 includes a plurality of storage clusters 35-1 through35-z. Each storage cluster includes a corresponding local communicationresource 26-1 through 26-z and a number of computing devices 18-1through 18-5. Each computing device executes an input, output, andprocessing (TO &P) processing function 34-1 through 34-5 to store andprocess data.

The number of computing devices in a storage cluster corresponds to thenumber of segments (e.g., a segment group) in which a data partitionedis divided. For example, if a data partition is divided into fivesegments, a storage cluster includes five computing devices. As anotherexample, if the data is divided into eight segments, then there areeight computing devices in the storage clusters.

To store a segment group of segments 29 within a storage cluster, adesignated computing device of the storage cluster interprets storageinstructions to identify computing devices (and/or processing coreresources thereof) for storing the segments to produce identifiedengaged resources. The designated computing device is selected by arandom selection, a default selection, a round-robin selection, or anyother mechanism for selection.

The designated computing device sends a segment to each computing devicein the storage cluster, including itself. Each of the computing devicesstores their segment of the segment group. As an example, five segments29 of a segment group are stored by five computing devices of storagecluster 35-1. The first computing device 18-1-1 stores a first segmentof the segment group; a second computing device 18-2-1 stores a secondsegment of the segment group; and so on. With the segments stored, thecomputing devices are able to process queries (e.g., query componentsfrom the Q&R sub-system 13) and produce appropriate result components.

While storage cluster 35-1 is storing and/or processing a segment group,the other storage clusters 35-2 through 35-n are storing and/orprocessing other segment groups. For example, a table is partitionedinto three segment groups. Three storage clusters store and/or processthe three segment groups independently. As another example, four tablesare independently storage and/or processed by one or more storageclusters. As yet another example, storage cluster 35-1 is storing and/orprocessing a second segment group while it is storing/or and processinga first segment group.

FIG. 7 is a schematic block diagram of an embodiment of a computingdevice 18 that includes a plurality of nodes 37-1 through 37-4 coupledto a computing device controller hub 36. The computing device controllerhub 36 includes one or more of a chipset, a quick path interconnect(QPI), and an ultra path interconnection (UPI). Each node 37-1 through37-4 includes a central processing module 39-1 through 39-4, a mainmemory 40-1 through 40-4 (e.g., volatile memory), a disk memory 38-1through 38-4 (non-volatile memory), and a network connection 41-1through 41-4. In an alternate configuration, the nodes share a networkconnection, which is coupled to the computing device controller hub 36or to one of the nodes as illustrated in subsequent figures.

In an embodiment, each node is capable of operating independently of theother nodes. This allows for large scale parallel operation of a queryrequest, which significantly reduces processing time for such queries.In another embodiment, one or more node function as co-processors toshare processing requirements of a particular function, or functions.

FIG. 8 is a schematic block diagram of another embodiment of a computingdevice is similar to the computing device of FIG. 7 with an exceptionthat it includes a single network connection 41, which is coupled to thecomputing device controller hub 36. As such, each node coordinates withthe computing device controller hub to transmit or receive data via thenetwork connection.

FIG. 9 is a schematic block diagram of another embodiment of a computingdevice is similar to the computing device of FIG. 7 with an exceptionthat it includes a single network connection 41, which is coupled to acentral processing module of a node (e.g., to central processing module39-1 of node 37-1). As such, each node coordinates with the centralprocessing module via the computing device controller hub 36 to transmitor receive data via the network connection.

FIG. 10 is a schematic block diagram of an embodiment of a node 37 ofcomputing device 18. The node 37 includes the central processing module39, the main memory 40, the disk memory 38, and the network connection41. The main memory 40 includes read only memory (RAM) and/or other formof volatile memory for storage of data and/or operational instructionsof applications and/or of the operating system. The central processingmodule 39 includes a plurality of processing modules 44-1 through 44-nand an associated one or more cache memory 45. A processing module is asdefined at the end of the detailed description.

The disk memory 38 includes a plurality of memory interface modules 43-1through 43-n and a plurality of memory devices 42-1 through 42-n (e.g.,non-volatile memory). The memory devices 42-1 through 42-n include, butare not limited to, solid state memory, disk drive memory, cloud storagememory, and other non-volatile memory. For each type of memory device, adifferent memory interface module 43-1 through 43-n is used. Forexample, solid state memory uses a standard, or serial, ATA (SATA),variation, or extension thereof, as its memory interface. As anotherexample, disk drive memory devices use a small computer system interface(SCSI), variation, or extension thereof, as its memory interface.

In an embodiment, the disk memory 38 includes a plurality of solid statememory devices and corresponding memory interface modules. In anotherembodiment, the disk memory 38 includes a plurality of solid statememory devices, a plurality of disk memories, and corresponding memoryinterface modules.

The network connection 41 includes a plurality of network interfacemodules 46-1 through 46-n and a plurality of network cards 47-1 through47-n. A network card includes a wireless LAN (WLAN) device (e.g., anIEEE 802.11n or another protocol), a LAN device (e.g., Ethernet), acellular device (e.g., CDMA), etc. The corresponding network interfacemodules 46-1 through 46-n include a software driver for thecorresponding network card and a physical connection that couples thenetwork card to the central processing module 39 or other component(s)of the node.

The connections between the central processing module 39, the mainmemory 40, the disk memory 38, and the network connection 41 may beimplemented in a variety of ways. For example, the connections are madethrough a node controller (e.g., a local version of the computing devicecontroller hub 36). As another example, the connections are made throughthe computing device controller hub 36.

FIG. 11 is a schematic block diagram of an embodiment of a node 37 of acomputing device 18 that is similar to the node of FIG. 10 , with adifference in the network connection. In this embodiment, the node 37includes a single network interface module 46 and a correspondingnetwork card 47 configuration.

FIG. 12 is a schematic block diagram of an embodiment of a node 37 of acomputing device 18 that is similar to the node of FIG. 10 , with adifference in the network connection. In this embodiment, the node 37connects to a network connection via the computing device controller hub36.

FIG. 13 is a schematic block diagram of another embodiment of a node 37of computing device 18 that includes processing core resources 48-1through 48-n, a memory device (MD) bus 49, a processing module (PM) bus50, a main memory 40 and a network connection 41. The network connection41 includes the network card 47 and the network interface module 46 ofFIG. 10 . Each processing core resource 48 includes a correspondingprocessing module 44-1 through 44-n, a corresponding memory interfacemodule 43-1 through 43-n, a corresponding memory device 42-1 through42-n, and a corresponding cache memory 45-1 through 45-n. In thisconfiguration, each processing core resource can operate independentlyof the other processing core resources. This further supports increasedparallel operation of database functions to further reduce executiontime.

The main memory 40 is divided into a computing device (CD) 56 sectionand a database (DB) 51 section. The database section includes a databaseoperating system (OS) area 52, a disk area 53, a network area 54, and ageneral area 55. The computing device section includes a computingdevice operating system (OS) area 57 and a general area 58. Note thateach section could include more or less allocated areas for varioustasks being executed by the database system.

In general, the database OS 52 allocates main memory for databaseoperations. Once allocated, the computing device OS 57 cannot accessthat portion of the main memory 40. This supports lock free andindependent parallel execution of one or more operations.

FIG. 14 is a schematic block diagram of an embodiment of operatingsystems of a computing device 18. The computing device 18 includes acomputer operating system 60 and a database overriding operating system(DB OS) 61. The computer OS 60 includes process management 62, filesystem management 63, device management 64, memory management 66, andsecurity 65. The processing management 62 generally includes processscheduling 67 and inter-process communication and synchronization 68. Ingeneral, the computer OS 60 is a conventional operating system used by avariety of types of computing devices. For example, the computeroperating system is a personal computer operating system, a serveroperating system, a tablet operating system, a cell phone operatingsystem, etc.

The database overriding operating system (DB OS) 61 includes custom DBdevice management 69, custom DB process management 70 (e.g., processscheduling and/or inter-process communication & synchronization), customDB file system management 71, custom DB memory management 72, and/orcustom security 73. In general, the database overriding OS 61 provideshardware components of a node for more direct access to memory, moredirect access to a network connection, improved independency, improveddata storage, improved data retrieval, and/or improved data processingthan the computing device OS.

In an example of operation, the database overriding OS 61 controls whichoperating system, or portions thereof, operate with each node and/orcomputing device controller hub of a computing device (e.g., via OSselect 75-1 through 75-n when communicating with nodes 37-1 through 37-nand via OS select 75-m when communicating with the computing devicecontroller hub 36). For example, device management of a node issupported by the computer operating system, while process management,memory management, and file system management are supported by thedatabase overriding operating system. To override the computer OS, thedatabase overriding OS provides instructions to the computer OSregarding which management tasks will be controlled by the databaseoverriding OS. The database overriding OS also provides notification tothe computer OS as to which sections of the main memory it is reservingexclusively for one or more database functions, operations, and/ortasks. One or more examples of the database overriding operating systemare provided in subsequent figures.

FIGS. 15-23 are schematic block diagrams of an example of processing atable or data set for storage in the database system 10. FIG. 15illustrates an example of a data set or table that includes 32 columnsand 80 rows, or records, that is received by the parallelized datainput-subsystem. This is a very small table, but is sufficient forillustrating one or more concepts regarding one or more aspects of adatabase system. The table is representative of a variety of dataranging from insurance data, to financial data, to employee data, tomedical data, and so on.

FIG. 16 illustrates an example of the parallelized data input-subsystemdividing the data set into two partitions. Each of the data partitionsincludes 40 rows, or records, of the data set. In another example, theparallelized data input-subsystem divides the data set into more thantwo partitions. In yet another example, the parallelized datainput-subsystem divides the data set into many partitions and at leasttwo of the partitions have a different number of rows.

FIG. 17 illustrates an example of the parallelized data input-subsystemdividing a data partition into a plurality of segments to form a segmentgroup. The number of segments in a segment group is a function of thedata redundancy encoding. In this example, the data redundancy encodingis single parity encoding from four data pieces; thus, five segments arecreated. In another example, the data redundancy encoding is a twoparity encoding from four data pieces; thus, six segments are created.In yet another example, the data redundancy encoding is single parityencoding from seven data pieces; thus, eight segments are created.

FIG. 18 illustrates an example of data for segment 1 of the segments ofFIG. 17 . The segment is in a raw form since it has not yet been keycolumn sorted. As shown, segment 1 includes 8 rows and 32 columns. Thethird column is selected as the key column and the other columns storedvarious pieces of information for a given row (i.e., a record). The keycolumn may be selected in a variety of ways. For example, the key columnis selected based on a type of query (e.g., a query regarding a year,where a data column is selected as the key column). As another example,the key column is selected in accordance with a received input commandthat identified the key column. As yet another example, the key columnis selected as a default key column (e.g., a date column, an ID column,etc.)

As an example, the table is regarding a fleet of vehicles. Each rowrepresents data regarding a unique vehicle. The first column stores avehicle ID, the second column stores make and model information of thevehicle. The third column stores data as to whether the vehicle is on oroff. The remaining columns store data regarding the operation of thevehicle such as mileage, gas level, oil level, maintenance information,routes taken, etc.

With the third column selected as the key column, the other columns ofthe segment are to be sorted based on the key column. Prior to sorted,the columns are separated to form data slabs. As such, one column isseparated out to form one data slab.

FIG. 19 illustrates an example of the parallelized data input-subsystemdividing segment 1 of FIG. 18 into a plurality of data slabs. A dataslab is a column of segment 1. In this figure, the data of the dataslabs has not been sorted. Once the columns have been separated intodata slabs, each data slab is sorted based on the key column. Note thatmore than one key column may be selected and used to sort the data slabsbased on two or more other columns.

FIG. 20 illustrates an example of the parallelized data input-subsystemsorting the each of the data slabs based on the key column. In thisexample, the data slabs are sorted based on the third column whichincludes data of “on” or “off”. The rows of a data slab are rearrangedbased on the key column to produce a sorted data slab. Each segment ofthe segment group is divided into similar data slabs and sorted by thesame key column to produce sorted data slabs.

FIG. 21 illustrates an example of each segment of the segment groupsorted into sorted data slabs. The similarity of data from segment tosegment is for the convenience of illustration. Note that each segmenthas its own data, which may or may not be similar to the data in theother sections.

FIG. 22 illustrates an example of a segment structure for a segment ofthe segment group. The segment structure for a segment includes the data& parity section, a manifest section, one or more index sections, and astatistics section. The segment structure represents a storage mappingof the data (e.g., data slabs and parity data) of a segment andassociated data (e.g., metadata, statistics, key column(s), etc.)regarding the data of the segment. The sorted data slabs of FIG. 16 ofthe segment are stored in the data & parity section of the segmentstructure. The sorted data slabs are stored in the data & parity sectionin a compressed format or as raw data (i.e., non-compressed format).Note that a segment structure has a particular data size (e.g., 32Giga-Bytes) and data is stored within coding block sizes (e.g., 4Kilo-Bytes).

Before the sorted data slabs are stored in the data & parity section, orconcurrently with storing in the data & parity section, the sorted dataslabs of a segment are redundancy encoded. The redundancy encoding maybe done in a variety of ways. For example, the redundancy encoding is inaccordance with RAID 5, RAID 6, or RAID 10. As another example, theredundancy encoding is a form of forward error encoding (e.g., ReedSolomon, Trellis, etc.). As another example, the redundancy encodingutilizes an erasure coding scheme. An example of redundancy encoding isdiscussed in greater detail with reference to one or more of FIGS. 29-36.

The manifest section stores metadata regarding the sorted data slabs.The metadata includes one or more of, but is not limited to, descriptivemetadata, structural metadata, and/or administrative metadata.Descriptive metadata includes one or more of, but is not limited to,information regarding data such as name, an abstract, keywords, author,etc. Structural metadata includes one or more of, but is not limited to,structural features of the data such as page size, page ordering,formatting, compression information, redundancy encoding information,logical addressing information, physical addressing information,physical to logical addressing information, etc. Administrative metadataincludes one or more of, but is not limited to, information that aids inmanaging data such as file type, access privileges, rights management,preservation of the data, etc.

The key column is stored in an index section. For example, a first keycolumn is stored in index #0. If a second key column exists, it isstored in index #1. As such, for each key column, it is stored in itsown index section. Alternatively, one or more key columns are stored ina single index section.

The statistics section stores statistical information regarding thesegment and/or the segment group. The statistical information includesone or more of, but is not limited, to number of rows (e.g., datavalues) in one or more of the sorted data slabs, average length of oneor more of the sorted data slabs, average row size (e.g., average sizeof a data value), etc. The statistical information includes informationregarding raw data slabs, raw parity data, and/or compressed data slabsand parity data.

FIG. 23 illustrates the segment structures for each segment of a segmentgroup having five segments. Each segment includes a data & paritysection, a manifest section, one or more index sections, and a statisticsection. Each segment is targeted for storage in a different computingdevice of a storage cluster. The number of segments in the segment groupcorresponds to the number of computing devices in a storage cluster. Inthis example, there are five computing devices in a storage cluster.Other examples include more or less than five computing devices in astorage cluster.

FIG. 24A illustrates an example of a query execution plan 2405implemented by the database system 10 to execute one or more queries byutilizing a plurality of nodes 37. Each node 37 can be utilized toimplement some or all of the plurality of nodes 37 of some or allcomputing devices 18-1-18-n, for example, of the of the parallelizeddata store, retrieve, and/or process sub-system 12, and/or of theparallelized query and results sub-system 13. The query execution plancan include a plurality of levels 2410. In this example, a plurality ofH levels in a corresponding tree structure of the query execution plan2405 are included. The plurality of levels can include a top, root level2412; a bottom, IO level 2416, and one or more inner levels 2414. Insome embodiments, there is exactly one inner level 2414, resulting in atree of exactly three levels 2410.1, 2410.2, and 2410.3, where level2410.H corresponds to level 2410.3. In such embodiments, level 2410.2 isthe same as level 2410.H-1, and there are no other inner levels2410.3-2410.H-2. Alternatively, any number of multiple inner levels 2414can be implemented to result in a tree with more than three levels.

This illustration of query execution plan 2405 illustrates the flow ofexecution of a given query by utilizing a subset of nodes across some orall of the levels 2410. In this illustration, nodes 37 with a solidoutline are nodes involved in executing a given query. Nodes 37 with adashed outline are other possible nodes that are not involved inexecuting the given query, but could be involved in executing otherqueries in accordance with their level of the query execution plan inwhich they are included.

Each of the nodes of IO level 2416 can be operable to, for a givenquery, perform the necessary row reads for gathering corresponding rowsof the query. These row reads can correspond to the segment retrieval toread some or all of the rows of retrieved segments determined to berequired for the given query. Thus, the nodes 37 in level 2416 caninclude any nodes 37 operable to retrieve segments for query executionfrom its own storage or from storage by one or more other nodes; torecover segment for query execution via other segments in the samesegment grouping by utilizing the redundancy error encoding scheme;and/or to determine which exact set of segments is assigned to the nodefor retrieval to ensure queries are executed correctly.

IO level 2416 can include all nodes in a given storage cluster 35 and/orcan include some or all nodes in multiple storage clusters 35, such asall nodes in a subset of the storage clusters 35-1-35-z and/or all nodesin all storage clusters 35-1-35-z. For example, all nodes 37 and/or allcurrently available nodes 37 of the database system 10 can be includedin level 2416. As another example, IO level 2416 can include a propersubset of nodes in the database system, such as some or all nodes thathave access to stored segments and/or that are included in a segment set35. In some cases, nodes 37 that do not store segments included insegment sets, that do not have access to stored segments, and/or thatare not operable to perform row reads are not included at the IO level,but can be included at one or more inner levels 2414 and/or root level2412.

The query executions discussed herein by nodes in accordance withexecuting queries at level 2416 can include retrieval of segments;extracting some or all necessary rows from the segments with some or allnecessary columns; and sending these retrieved rows to a node at thenext level 2410.H-1 as the query resultant generated by the node 37. Foreach node 37 at IO level 2416, the set of raw rows retrieved by the node37 can be distinct from rows retrieved from all other nodes, forexample, to ensure correct query execution. The total set of rows and/orcorresponding columns retrieved by nodes 37 in the IO level for a givenquery can be dictated based on the domain of the given query, such asone or more tables indicated in one or more SELECT statements of thequery, and/or can otherwise include all data blocks that are necessaryto execute the given query.

Each inner level 2414 can include a subset of nodes 37 in the databasesystem 10. Each level 2414 can include a distinct set of nodes 37 and/orsome or more levels 2414 can include overlapping sets of nodes 37. Thenodes 37 at inner levels are implemented, for each given query, toexecute queries in conjunction with operators for the given query. Forexample, a query operator execution flow can be generated for a givenincoming query, where an ordering of execution of its operators isdetermined, and this ordering is utilized to assign one or moreoperators of the query operator execution flow to each node in a giveninner level 2414 for execution. For example, each node at a same innerlevel can be operable to execute a same set of operators for a givenquery, in response to being selected to execute the given query, uponincoming resultants generated by nodes at a directly lower level togenerate its own resultants sent to a next higher level. In particular,each node at a same inner level can be operable to execute a sameportion of a same query operator execution flow for a given query. Incases where there is exactly one inner level, each node selected toexecute a query at a given inner level performs some or all of the givenquery's operators upon the raw rows received as resultants from thenodes at the IO level, such as the entire query operator execution flowand/or the portion of the query operator execution flow performed upondata that has already been read from storage by nodes at the IO level.In some cases, some operators beyond row reads are also performed by thenodes at the IO level. Each node at a given inner level 2414 can furtherperform a gather function to collect, union, and/or aggregate resultantssent from a previous level, for example, in accordance with one or morecorresponding operators of the given query.

The root level 2412 can include exactly one node for a given query thatgathers resultants from every node at the top-most inner level 2414. Thenode 37 at root level 2412 can perform additional query operators of thequery and/or can otherwise collect, aggregate, and/or union theresultants from the top-most inner level 2414 to generate the finalresultant of the query, which includes the resulting set of rows and/orone or more aggregated values, in accordance with the query, based onbeing performed on all rows required by the query. The root level nodecan be selected from a plurality of possible root level nodes, wheredifferent root nodes are selected for different queries. Alternatively,the same root node can be selected for all queries.

As depicted in FIG. 24A, resultants are sent by nodes upstream withrespect to the tree structure of the query execution plan as they aregenerated, where the root node generates a final resultant of the query.While not depicted in FIG. 24A, nodes at a same level can share dataand/or send resultants to each other, for example, in accordance withoperators of the query at this same level dictating that data is sentbetween nodes.

In some cases, the IO level 2416 always includes the same set of nodes37, such as a full set of nodes and/or all nodes that are in a storagecluster 35 that stores data required to process incoming queries. Insome cases, the lowest inner level corresponding to level 2410.H-1includes at least one node from the IO level 2416 in the possible set ofnodes. In such cases, while each selected node in level 2410.H-1 isdepicted to process resultants sent from other nodes 37 in FIG. 24A,each selected node in level 2410.H-1 that also operates as a node at theIO level further performs its own row reads in accordance with its queryexecution at the IO level, and gathers the row reads received asresultants from other nodes at the IO level with its own row reads forprocessing via operators of the query. One or more inner levels 2414 canalso include nodes that are not included in IO level 2416, such as nodes37 that do not have access to stored segments and/or that are otherwisenot operable and/or selected to perform row reads for some or allqueries.

The node 37 at root level 2412 can be fixed for all queries, where theset of possible nodes at root level 2412 includes only one node thatexecutes all queries at the root level of the query execution plan.Alternatively, the root level 2412 can similarly include a set ofpossible nodes, where one node selected from this set of possible nodesfor each query and where different nodes are selected from the set ofpossible nodes for different queries. In such cases, the nodes at innerlevel 2410.2 determine which of the set of possible root nodes to sendtheir resultant to. In some cases, the single node or set of possiblenodes at root level 2412 is a proper subset of the set of nodes at innerlevel 2410.2, and/or is a proper subset of the set of nodes at the IOlevel 2416. In cases where the root node is included at inner level2410.2, the root node generates its own resultant in accordance withinner level 2410.2, for example, based on multiple resultants receivedfrom nodes at level 2410.3, and gathers its resultant that was generatedin accordance with inner level 2410.2 with other resultants receivedfrom nodes at inner level 2410.2 to ultimately generate the finalresultant in accordance with operating as the root level node.

In some cases where nodes are selected from a set of possible nodes at agiven level for processing a given query, the selected node must havebeen selected for processing this query at each lower level of the queryexecution tree. For example, if a particular node is selected to processa node at a particular inner level, it must have processed the query togenerate resultants at every lower inner level and the IO level. In suchcases, each selected node at a particular level will always use its ownresultant that was generated for processing at the previous, lowerlevel, and will gather this resultant with other resultants receivedfrom other child nodes at the previous, lower level. Alternatively,nodes that have not yet processed a given query can be selected forprocessing at a particular level, where all resultants being gatheredare therefore received from a set of child nodes that do not include theselected node.

The configuration of query execution plan 2405 for a given query can bedetermined in a downstream fashion, for example, where the tree isformed from the root downwards. Nodes at corresponding levels aredetermined from configuration information received from correspondingparent nodes and/or nodes at higher levels, and can each sendconfiguration information to other nodes, such as their own child nodes,at lower levels until the lowest level is reached. This configurationinformation can include assignment of a particular subset of operatorsof the set of query operators that each level and/or each node willperform for the query. The execution of the query is performed upstreamin accordance with the determined configuration, where IO reads areperformed first, and resultants are forwarded upwards until the rootnode ultimately generates the query result.

FIG. 24B illustrates an embodiment of a node 37 executing a query inaccordance with the query execution plan 2405 by implementing a queryprocessing module 2435. The query processing module 2435 can be operableto execute a query operator execution flow 2433 determined by the node37, where the query operator execution flow 2433 corresponds to theentirety of processing of the query upon incoming data assigned to thecorresponding node 37 in accordance with its role in the query executionplan 2405. This embodiment of node 37 that utilizes a query processingmodule 2435 can be utilized to implement some or all of the plurality ofnodes 37 of some or all computing devices 18-1-18-n, for example, of theof the parallelized data store, retrieve, and/or process sub-system 12,and/or of the parallelized query and results sub-system 13.

As used herein, execution of a particular query by a particular node 37can correspond to the execution of the portion of the particular queryassigned to the particular node in accordance with full execution of thequery by the plurality of nodes involved in the query execution plan2405. This portion of the particular query assigned to a particular nodecan correspond to execution plurality of operators indicated by a queryoperator execution flow 2433. In particular, the execution of the queryfor a node 37 at an inner level 2414 and/or root level 2412 correspondsto generating a resultant by processing all incoming resultants receivedfrom nodes at a lower level of the query execution plan 2405 that sendtheir own resultants to the node 37. The execution of the query for anode 37 at the IO level corresponds to generating all resultant datablocks by retrieving and/or recovering all segments assigned to the node37.

Thus, as used herein, a node 37's full execution of a given querycorresponds to only a portion of the query's execution across all nodesin the query execution plan 2405. In particular, a resultant generatedby an inner level node 37's execution of a given query may correspond toonly a portion of the entire query result, such as a subset of rows in afinal result set, where other nodes generate their own resultants togenerate other portions of the full resultant of the query. In suchembodiments, a plurality of nodes at this inner level can fully executequeries on different portions of the query domain independently inparallel by utilizing the same query operator execution flow 2433.Resultants generated by each of the plurality of nodes at this innerlevel 2414 can be gathered into a final result of the query, forexample, by the node 37 at root level 2412 if this inner level is thetop-most inner level 2414 or the only inner level 2414. As anotherexample, resultants generated by each of the plurality of nodes at thisinner level 2414 can be further processed via additional operators of aquery operator execution flow 2433 being implemented by another node ata consecutively higher inner level 2414 of the query execution plan2405, where all nodes at this consecutively higher inner level 2414 allexecute their own same query operator execution flow 2433.

As discussed in further detail herein, the resultant generated by a node37 can include a plurality of resultant data blocks generated via aplurality of partial query executions. As used herein, a partial queryexecution performed by a node corresponds to generating a resultantbased on only a subset of the query input received by the node 37. Inparticular, the query input corresponds to all resultants generated byone or more nodes at a lower level of the query execution plan that sendtheir resultants to the node. However, this query input can correspondto a plurality of input data blocks received over time, for example, inconjunction with the one or more nodes at the lower level processingtheir own input data blocks received over time to generate theirresultant data blocks sent to the node over time. Thus, the resultantgenerated by a node's full execution of a query can include a pluralityof resultant data blocks, where each resultant data block is generatedby processing a subset of all input data blocks as a partial queryexecution upon the subset of all data blocks via the query operatorexecution flow 2433.

As illustrated in FIG. 24B, the query processing module 2435 can beimplemented by a single processing core resource 48 of the node 37. Insuch embodiments, each one of the processing core resources 48-1-48-n ofa same node 37 can be executing at least one query concurrently viatheir own query processing module 2435, where a single node 37implements each of set of operator processing modules 2435-1-2435-n viaa corresponding one of the set of processing core resources 48-1-48-n. Aplurality of queries can be concurrently executed by the node 37, whereeach of its processing core resources 48 can each independently executeat least one query within a same temporal period by utilizing acorresponding at least one query operator execution flow 2433 togenerate at least one query resultant corresponding to the at least onequery.

FIG. 25C illustrates a particular example of a node 37 at the IO level2416 of the query execution plan 2405 of FIG. 24A. A node 37 can utilizeits own memory resources, such as some or all of its disk memory 38and/or some or all of its main memory 40 to implement at least onememory drive 2425 that stores a plurality of segments 2424. Memorydrives 2425 of a node 37 can be implemented, for example, by utilizingdisk memory 38 and/or main memory 40. In particular, a plurality ofdistinct memory drives 2425 of a node 37 can be implemented via theplurality of memory devices 42-1-42-n of the node 37's disk memory 38.

Each segment 2424 stored in memory drive 2425 can be generated asdiscussed previously in conjunction with FIGS. 15-23 . A plurality ofrecords 2422 can be included in and/or extractable from the segment, forexample, where the plurality of records 2422 of a segment 2424correspond to a plurality of rows designated for the particular segment2424 prior to applying the redundancy storage coding scheme asillustrated in FIG. 17 . The records 2422 can be included in data ofsegment 2424, for example, in accordance with a column-format and/orother structured format. Each segments 2424 can further include paritydata 2426 as discussed previously to enable other segments 2424 in thesame segment group to be recovered via applying a decoding functionassociated with the redundancy storage coding scheme, such as a RAIDscheme and/or erasure coding scheme, that was utilized to generate theset of segments of a segment group.

Thus, in addition to performing the first stage of query execution bybeing responsible for row reads, nodes 37 can be utilized for databasestorage, and can each locally store a set of segments in its own memorydrives 2425. In some cases, a node 37 can be responsible for retrievalof only the records stored in its own one or more memory drives 2425 asone or more segments 2424. Executions of queries corresponding toretrieval of records stored by a particular node 37 can be assigned tothat particular node 37. In other embodiments, a node 37 does not useits own resources to store segments. A node 37 can access its assignedrecords for retrieval via memory resources of another node 37 and/or viaother access to memory drives 2425, for example, by utilizing systemcommunication resources 14.

The query processing module 2435 of the node 37 can be utilized to readthe assigned by first retrieving or otherwise accessing thecorresponding redundancy-coded segments 2424 that include the assignedrecords its one or more memory drives 2425. Query processing module 2435can include a record extraction module 2438 that is then utilized toextract or otherwise read some or all records from these segments 2424accessed in memory drives 2425, for example, where record data of thesegment is segregated from other information such as parity dataincluded in the segment and/or where this data containing the records isconverted into row-formatted records from the column-formatted row datastored by the segment. Once the necessary records of a query are read bythe node 37, the node can further utilize query processing module 2435to send the retrieved records all at once, or in a stream as they areretrieved from memory drives 2425, as data blocks to the next node 37 inthe query execution plan 2405 via system communication resources 14 orother communication channels.

FIG. 24D illustrates an embodiment of a node 37 that implements asegment recovery module 2439 to recover some or all segments that areassigned to the node for retrieval, in accordance with processing one ormore queries, that are unavailable. Some or all features of the node 37of FIG. 24D can be utilized to implement the node 37 of FIGS. 24B and24C, and/or can be utilized to implement one or more nodes 37 of thequery execution plan 2405 of FIG. 24A, such as nodes 37 at the IO level2416. A node 37 may store segments on one of its own memory drives 2425that becomes unavailable, or otherwise determines that a segmentassigned to the node for execution of a query is unavailable for accessvia a memory drive the node 37 accesses via system communicationresources 14. The segment recovery module 2439 can be implemented via atleast one processing module of the node 37, such as resources of centralprocessing module 39. The segment recovery module 2439 can retrieve thenecessary number of segments 1-K in the same segment group as anunavailable segment from other nodes 37, such as a set of other nodes37-1-37-K that store segments in the same storage cluster 35. Usingsystem communication resources 14 or other communication channels, a setof external retrieval requests 1-K for this set of segments 1-K can besent to the set of other nodes 37-1-37-K, and the set of segments can bereceived in response. This set of K segments can be processed, forexample, where a decoding function is applied based on the redundancystorage coding scheme utilized to generate the set of segments in thesegment group and/or parity data of this set of K segments is otherwiseutilized to regenerate the unavailable segment. The necessary recordscan then be extracted from the unavailable segment, for example, via therecord extraction module 2438, and can be sent as data blocks to anothernode 37 for processing in conjunction with other records extracted fromavailable segments retrieved by the node 37 from its own memory drives2425.

Note that the embodiments of node 37 discussed herein can be configuredto execute multiple queries concurrently by communicating with nodes 37in the same or different tree configuration of corresponding queryexecution plans and/or by performing query operations upon data blocksand/or read records for different queries. In particular, incoming datablocks can be received from other nodes for multiple different queriesin any interleaving order, and a plurality of operator executions uponincoming data blocks for multiple different queries can be performed inany order, where output data blocks are generated and sent to the sameor different next node for multiple different queries in anyinterleaving order. IO level nodes can access records for the same ordifferent queries any interleaving order. Thus, at a given point intime, a node 37 can have already begun its execution of at least twoqueries, where the node 37 has also not yet completed its execution ofthe at least two queries.

A query execution plan 2405 can guarantee query correctness based onassignment data sent to or otherwise communicated to all nodes at the IOlevel ensuring that the set of required records in query domain data ofa query, such as one or more tables required to be accessed by a query,are accessed exactly one time: if a particular record is accessedmultiple times in the same query and/or is not accessed, the queryresultant cannot be guaranteed to be correct. Assignment data indicatingsegment read and/or record read assignments to each of the set of nodes37 at the IO level can be generated, for example, based on beingmutually agreed upon by all nodes 37 at the IO level via a consensusprotocol executed between all nodes at the IO level and/or distinctgroups of nodes 37 such as individual storage clusters 35. Theassignment data can be generated such that every record in the databasesystem and/or in query domain of a particular query is assigned to beread by exactly one node 37. Note that the assignment data may indicatethat a node 37 is assigned to read some segments directly from memory asillustrated in FIG. 24C and is assigned to recover some segments viaretrieval of segments in the same segment group from other nodes 37 andvia applying the decoding function of the redundancy storage codingscheme as illustrated in FIG. 24D.

Assuming all nodes 37 read all required records and send their requiredrecords to exactly one next node 37 as designated in the query executionplan 2405 for the given query, the use of exactly one instance of eachrecord can be guaranteed. Assuming all inner level nodes 37 process allthe required records received from the corresponding set of nodes 37 inthe IO level 2416, via applying one or more query operators assigned tothe node in accordance with their query operator execution flow 2433,correctness of their respective partial resultants can be guaranteed.This correctness can further require that nodes 37 at the same levelintercommunicate by exchanging records in accordance with JOINoperations as necessary, as records received by other nodes may berequired to achieve the appropriate result of a JOIN operation. Finally,assuming the root level node receives all correctly generated partialresultants as data blocks from its respective set of nodes at thepenultimate, highest inner level 2414 as designated in the queryexecution plan 2405, and further assuming the root level nodeappropriately generates its own final resultant, the correctness of thefinal resultant can be guaranteed.

In some embodiments, each node 37 in the query execution plan canmonitor whether it has received all necessary data blocks to fulfill itsnecessary role in completely generating its own resultant to be sent tothe next node 37 in the query execution plan. A node 37 can determinereceipt of a complete set of data blocks that was sent from a particularnode 37 at an immediately lower level, for example, based on beingnumbered and/or have an indicated ordering in transmission from theparticular node 37 at the immediately lower level, and/or based on afinal data block of the set of data blocks being tagged in transmissionfrom the particular node 37 at the immediately lower level to indicateit is a final data block being sent. A node 37 can determine therequired set of lower level nodes from which it is to receive datablocks based on its knowledge of the query execution plan 2405 of thequery. A node 37 can thus conclude when complete set of data blocks hasbeen received each designated lower level node in the designated set asindicated by the query execution plan 2405. This node 37 can thereforedetermine itself that all required data blocks have been processed intodata blocks sent by this node 37 to the next node 37 and/or as a finalresultant if this node 37 is the root node. This can be indicated viatagging of its own last data block, corresponding to the final portionof the resultant generated by the node, where it is guaranteed that allappropriate data was received and processed into the set of data blockssent by this node 37 in accordance with applying its own query operatorexecution flow 2433.

In some embodiments, if any node 37 determines it did not receive all ofits required data blocks, the node 37 itself cannot fulfill generationof its own set of required data blocks. For example, the node 37 willnot transmit a final data block tagged as the “last” data block in theset of outputted data blocks to the next node 37, and the next node 37will thus conclude there was an error and will not generate a full setof data blocks itself. The root node, and/or these intermediate nodesthat never received all their data and/or never fulfilled theirgeneration of all required data blocks, can independently determine thequery was unsuccessful. In some cases, the root node, upon determiningthe query was unsuccessful, can initiate re-execution of the query byre-establishing the same or different query execution plan 2405 in adownward fashion as described previously, where the nodes 37 in thisre-established query execution plan 2405 execute the query accordinglyas though it were a new query. For example, in the case of a nodefailure that caused the previous query to fail, the new query executionplan 2405 can be generated to include only available nodes where thenode that failed is not included in the new query execution plan 2405.

FIG. 25A illustrates an embodiment of a database system 10 thatimplements a record processing and storage system 2505. The recordprocessing and storage system 2505 can be operable to generate and storethe segments 2424 discussed previously by utilizing a segment generator2517 to convert sets of row-formatted records 2422 into column-formattedrecord data 2565. These row-formatted records 2422 can correspond torows of a database table with populated column values of the table, forexample, where each record 2422 corresponds to a single row asillustrated in FIG. 15 . For example, the segment generator 2517 cangenerate the segments 2424 in accordance with the process discussed inconjunction with FIGS. 15-23 . The segments 2424 can be generated toinclude index data 2518, which can include a plurality of index sectionssuch as the index sections 0-X illustrated in FIG. 23 . The segments2424 can optionally be generated to include other metadata, such as themanifest section and/or statistics section illustrated in FIG. 23 .

The generated segments 2424 can be stored in a segment storage system2508 for access in query executions. For example, the records 2422 canbe extracted from generated segments 2424 in various query executionsperformed by via a query processing system 2502 of the database system10, for example, as discussed in FIGS. 25A-25D. In particular, thesegment storage system 2508 can be implemented by utilizing the memorydrives 2425 of a plurality of IO level nodes 27 that are operable tostore segments. As discussed previously, nodes 37 at the IO level 2416can store segments 2424 in their memory drives 2425 as illustrated inFIG. 24C. These nodes can perform IO operations in accordance with queryexecutions by reading rows from these segments 2424 and/or by recoveringsegments based on receiving segments from other nodes as illustrated inFIG. 24D. The records 2422 can be extracted from the column-formattedrecord data 2565 for these IO operations of query executions byutilizing the index data 2518 of the corresponding segment 2424.

To enhance the performance of query executions via access to segments2424 to read records 2422 in this fashion, the sets of rows included ineach segment are ideally clustered well. In the ideal case, rows sharingthe same cluster key are stored together in the same segment or samegroup of segments. For example, rows having matching values of keycolumns(s) of FIG. 18 utilized to sort the rows into groups forconversion into segments are ideally stored in the same segments. Asused herein, a cluster key can be implemented as any one or morecolumns, such as key columns(s) of FIG. 18 , that are utilized tocluster records into segment groups for segment generation. As usedherein, more favorable levels of clustering correspond to more rows withsame or similar cluster keys being stored in the same segments, whileless favorable levels of clustering correspond to less rows with same orsimilar cluster keys being stored in the same segments. More favorablelevels of clustering can achieve more efficient query performance. Inparticular, query filtering parameters of a given query can specifyparticular sets of records with particular cluster keys be accessed, andif these records are stored together, fewer segments, memory drives,and/or nodes need to be accessed and/or utilized for the given query.

These favorable levels of clustering can be hard to achieve when relyingupon the incoming ordering of records in record streams 1-L from a setof data sources 2501-1-2501-L. No assumptions can necessarily be madeabout the clustering, with respect to the cluster key, of rows presentedby external sources as they are received in the data stream. Forexample, the cluster key value of a given row received at a first timet₁ gives no information about the cluster key value of a row received ata second time t₂ after t₁. It would therefore be unideal to frequentlygenerate segments by performing a clustering process to group the mostrecently received records by cluster key. In particular, because recordsreceived within a given time frame from a particular data source may notbe related and have many different cluster key values, the resultingrecord groups utilized to generate segments would render unfavorablelevels of clustering.

To achieve more favorable levels of clustering, the record processingand storage system 2505 implements a page generator 2511 and a pagestorage system 2506 to store a plurality of pages 2515. The pagegenerator 2511 is operable to generate pages 2515 from incoming records2422 of record streams 1-L, for example, as is discussed in furtherdetail in conjunction with FIG. 25C. Each page 2515 generated by thepage generator 2511 can include a set of records, for example, in theiroriginal row format and/or in a data format as received from datasources 2501-1-2501-L. Once generated, the pages 2515 can be stored in apage storage system 2506, which can be implemented via memory drivesand/or cache memory of one or more computing devices 18, such as some orall of the same or different nodes 37 storing segments 2424 as part ofthe segment storage system 2508.

This generation and storage of pages 2515 stored by can serve astemporary storage of the incoming records as they await conversion intosegments 2424. Pages 2515 can be generated and stored over lengthyperiods of time, such as hours or days. During this length time frame,pages 2515 can continue to be accumulated as one or more record streamsof incoming records 1-L continue to supply additional records forstorage by the database system.

The plurality of pages generated and stored over this period of time canbe converted into segments, for example once a sufficient amount ofrecords have been received and stored as pages, and/or once the pagestorage system 2506 runs out of memory resources to store any additionalpages. It can be advantageous to accumulate and store as many records aspossible in pages 2515 prior to conversion to achieve more favorablelevels of clustering. In particular, performing a clustering processupon a greater numbers of records, such as the greatest number ofrecords possible can achieve more favorable levels of clustering, Forexample, greater numbers of records with common cluster keys areexpected to be included in the total set of pages 2515 of the pagestorage system 2506 when the page storage system 2506 accumulates pagesover longer periods of time to include a greater number of pages. Inother words. delaying the grouping of rows into segments as long aspossible increases the chances of having sufficient numbers of recordswith same and/or similar cluster keys to group together in segments.Determining when to generate segments such that the conversion frompages into segments is delayed as long as possible, and/or such that asufficient amount of records are converted all at once to induce morefavorable levels of cluster, is discussed in further detail inconjunction with FIGS. 26A-26D. Alternatively, the conversion of pagesinto segments can occur at any frequency, for example, where pages areconverted into segments more frequently and/or in accordance with anyschedule or determination in other embodiments of the record processingand storage system 2505.

This mechanism of improving clustering levels in segment generation bydelaying the clustering process required for segment generation as longas possible can be further leveraged to reduce resource utilization ofthe record processing and storage system 2505. As the record processingand storage system 2505 is responsible for receiving records streamsfrom data sources for storage, for example, in the scale of terabyte persecond load rates, this process of generating pages from the recordstreams should therefore be as efficient as possible. The page generator2511 can be further implemented to reduce resource consumption of therecord processing and storage system 2505 in page generation and storageby minimizing the processing of, movement of, and/or access to records2422 of pages 2515 once generated as they await conversion intosegments.

To reduce the processing induced upon the record processing and storagesystem 2505 during this data ingress, sets of incoming records 2422 canbe included in a corresponding page 2515 without performing anyclustering or sorting. For example, as clustering assumptions cannot bemade for incoming data, incoming rows can be placed into pages based onthe order that they are received and/or based on any order that bestconserves resources. In some embodiments, the entire clustering processis performed by the segment generator 2517 upon all stored pages all atonce, where the page generator 2511 does not perform any stages of theclustering process.

In some embodiments, to further reduce the processing induced upon therecord processing and storage system 2505 during this data ingress,incoming record data of data streams 1-L undergo minimal reformatting bythe page generator 2511 in generating pages 2515. In some cases, theincoming data of record streams 1-L is not reformatted and is simply“placed” into a corresponding page 2515. For example, a set of recordsare included in given page in accordance with formatted row datareceived from data sources.

While delaying segment generation in this fashion improves clusteringand further improves ingress efficiency, it can be unideal to wait forrecords to be processed into segments before they appear in queryresults, particularly because the most recent data may be of the mostinterest to end users requesting queries. The record processing andstorage system 2505 can resolve this problem by being further operableto facilitate page reads in addition to segment reads in facilitatingquery executions.

As illustrated in FIG. 25A, a query processing system 2502 can implementa query execution plan generator module 2503 to generate query executionplan data based on a received query request. The query execution plandata can be relayed to nodes participating in the corresponding queryexecution plan 2405 indicated by the query execution plan data, forexample, as discussed in conjunction with FIG. 24A. A query executionmodule 2504 can be implemented via a plurality of nodes participating inthe query execution plan 2405, for example, where data blocks arepropagated upwards from nodes at IO level 2416 to a root node at rootlevel 2412 to generate a query resultant. The nodes at IO level 2416 canperform row reads to read records 2422 from segments 2424 as discussedpreviously and as illustrated in FIG. 24C. The nodes at IO level 2416can further perform row reads to read records 2422 from pages 2515. Forexample, once records 2422 are durably stored by being stored in a page2515, and/or by being duplicated and stored in multiple pages 2515, therecord 2422 can be available to service queries, and will be accessed bynodes 37 at IO level 2416 in executing queries accordingly. This enablesthe availability of records 2422 for query executions more quickly,where the records need not be processed for storage in their finalstorage format as segments 2424 to be accessed in query requests.Execution of a given query can include utilizing a set of records storedin a combination of pages 2515 and segments 2424. An embodiment of an IOlevel node that stores and accesses both segments and pages isillustrated in FIG. 25E.

The record processing and storage system 2505 can be implementedutilizing the parallelized data input sub-system 11 and/or theparallelized ingress sub-system 24 of FIG. 4 . The record processing andstorage system 2505 can alternatively or additionally be implementedutilizing the parallelized data store, retrieve, and/or processsub-system 12 of FIG. 6 . The record processing and storage system 2505can alternatively or additionally be implemented by utilizing one ormore computing devices 18 and/or by utilizing one or more nodes 37.

The record processing and storage system 2505 can be otherwiseimplemented utilizing at least one processor and at least one memory.For example, the at least one memory can store operational instructionsthat, when executed by the at least one processor, cause the recordprocessing and storage system to perform some or all of thefunctionality described herein, such as some or all of the functionalityof the page generator 2511 and/or of the segment generator 2517discussed herein. In some cases, one or more individual nodes 37 and/orone or more individual processing core resources 48 can be operable toperform some or all of the functionality of the record processing andstorage system 2505, such as some or all of the functionality of thepage generator 2511 and/or of the segment generator 2517, independentlyor in tandem by utilizing their own processing resources and/or memoryresources.

The query processing system 2502 can be alternatively or additionallyimplemented utilizing the parallelized query and results sub-system 13of FIG. 5 . The query processing system 2502 can be alternatively oradditionally implemented utilizing the parallelized data store,retrieve, and/or process sub-system 12 of FIG. 6 . The query processingsystem 2502 can alternatively or additionally be implemented byutilizing one or more computing devices 18 and/or by utilizing one ormore nodes 37.

The query processing system 2502 can be otherwise implemented utilizingat least one processor and at least one memory. For example, the atleast one memory can store operational instructions that, when executedby the at least one processor, cause the record processing and storagesystem to perform some or all of the functionality described herein,such as some or all of the functionality of the query execution plangenerator module 2503 and/or of the query execution module 2504discussed herein. In some cases, one or more individual nodes 37 and/orone or more individual processing core resources 48 can be operable toperform some or all of the functionality of the query processing system2502, such as some or all of the functionality of query execution plangenerator module 2503 and/or of the query execution module 2504,independently or in tandem by utilizing their own processing resourcesand/or memory resources.

In some embodiments, one or more nodes 37 of the database system 10 asdiscussed herein can be operable to perform multiple functionalities ofthe database system 10 illustrated in FIG. 25A. For example, a singlenode can be utilized to implement the page generator 2511, the pagestorage system 2506, the segment generator 2517, the segment storagesystem 2508, the query execution plan generator module, and/or the queryexecution module 2504 as a node 37 at one or more levels 2410 of a queryexecution plan 2405. In particular, the single node can utilizedifferent processing core resources 48 to implement differentfunctionalities in parallel, and/or can utilize the same processing coreresources 48 to implement different functionalities at different times.

Some or all data sources 2501 can implemented utilizing at least oneprocessor and at least one memory. Some or all data sources 2501 can beexternal from database system 10 and/or can be included as part ofdatabase system 10. For example, the at least one memory of a datasource 2501 can store operational instructions that, when executed bythe at least one processor of the data source 2501, cause the datasource 2501 to perform some or all of the functionality of data sources2501 described herein. In some cases, data sources 2501 can receiveapplication data from the database system 10 for download, storage,and/or installation. Execution of the stored application data byprocessing modules of data sources 2501 can cause the data sources 2501to execute some or all of the functionality of data sources 2501discussed herein.

In some embodiments, system communication resources 14, externalnetwork(s) 17, local communication resources 25, wide area networks 22,and/or other communication resources of database system 10 can beutilized to facilitate any transfer of data by the record processing andstorage system 2505. This can include, for example: transmission ofrecord streams 1-L from data sources 2501 to the record processing andstorage system 2505; transfer of pages 2515 to page storage system 2506once generated by the page generator 2511; access to pages 2515 by thesegment generator 2517; transfer of segments 2424 to the segment storagesystem 2508 once generated by the segment generator 2517; communicationof query execution plan data to the query execution module 2504, such asthe plurality of nodes 37 of the corresponding query execution plan2405; reading of records by the query execution module 2504, such as IOlevel nodes 37, via access to pages 2515 stored page storage system 2506and/or via access to segments 2424 stored segment storage system 2508;sending of data blocks generated by nodes 37 of the corresponding queryexecution plan 2405 to other nodes 37 in conjunction with theirexecution of the query; and/or any other accessing of data,communication of data, and/or transfer of data by record processing andstorage system 2505 and/or within the record processing and storagesystem 2505 as discussed herein.

FIG. 25B illustrates an example embodiment of the record processing andstorage system 2505 of FIG. 25A. Some or all of the features illustratedand discussed in conjunction with the record processing and storagesystem 2505 FIG. 25B can be utilized to implement the record processingand storage system 2505 and/or any other embodiment of the recordprocessing and storage system 2505 described herein.

The record processing and storage system 2505 can include a plurality ofstream loader modules 2510-1-2510-N. Each stream loader module 2510 canbe implemented via its own processing and/or memory resources. Forexample, each stream loader module 2510 can be implemented via its owncomputing device 18, via its own node 37, and/or via its own processingcore resource 48. The plurality of stream loader modules 2510-1-2510-Ncan be implemented to perform some or all of the functionality of therecord processing and storage system 2505 in a parallelized fashion.

The record processing and storage system 2505 can include queue reader2559, a plurality of stateful file readers 2556-1-2556-N, and/orstand-alone file readers 2558-1-2558-N. For example, the queue reader2559, a plurality of stateful file readers 2556-1-2556-N, and/orstand-alone file readers 2558-1-2558-N are utilized to enable eachstream loader modules 2510 to receive one or more of the record streams1-L received from the data sources 2501-1-2501-L as illustrated in FIG.25A. For example, each stream loader module 2510 receives a distinctsubset of the entire set of records received by the record processingand storage system 2505 at a given time.

Each stream loader module 2510 can receive records 2422 in one or morerecord streams via its own stateful file reader 2556 and/or stand-alonefile reader 2558. Each stream loader module 2510 can optionally receiverecords 2422 and/or otherwise communicate with a common queue reader2559. Each stateful file reader 2556 can communicate with a metadatacluster 2552 that includes data supplied by and/or corresponding to aplurality of administrators 2554-1-2554-M. The metadata cluster 2552 canbe implemented by utilizing the administrative processing sub-system 15and/or the configuration sub-system 16. The queue reader 2559, eachstateful file reader 2556, and/or each stand-alone file reader 2558 canbe implemented utilizing the parallelized ingress sub-system 24 and/orthe parallelized data input sub-system 11. The metadata cluster 2552,the queue reader 2559, each stateful file reader 2556, and/or eachstand-alone file reader 2558 can be implemented utilizing at least onecomputing device 18 and/or at least one node 37. In cases where a givenstream loader module 2510 is implemented via its own computing device 18and/or node 37, the same computing device 18 and/or node 37 canoptionally be utilized to implement the stateful file reader 2556,and/or each stand-alone file reader 2558 communicating with the givenstream loader module 2510.

Each stream loader module 2510 can implement its own page generator2511, its own index generator 2513, and/or its own segment generator2517, for example, by utilizing its own processing and/or memoryresources such as the processing and/or memory resources of acorresponding computing device 18. For example, the page generator 2511of FIG. 25A can be implemented as a plurality of page generators 2511 ofa corresponding plurality of stream loader modules 2510 as illustratedin FIG. 25B. Each page generator 2511 of FIG. 25B can process its ownincoming records 2422 to generate its own corresponding pages 2515.

As pages 2515 are generated by the page generator 2511 of a streamloader module 2510, they can be stored in a page cache 2512. The pagecache 2512 can be implemented utilizing memory resources of the streamloader module 2510, such as memory resources of the correspondingcomputing device 18. For example, the page cache 2512 of each streamloader module 2010-1-2010-N can individually or collectively implementsome or all of the page storage system 2506 of FIG. 25A.

The segment generator 2517 of FIG. 25A can similarly be implemented as aplurality of segment generators 2517 of a corresponding plurality ofstream loader modules 2510 as illustrated in FIG. 25B. Each segmentgenerator 2517 of FIG. 25B can generate its own set of segments2424-1-2424-J included in one or more segment groups 2522. The segmentgroup 2522 can be implemented as the segment group of FIG. 23 , forexample, where J is equal to five or another number of segmentsconfigured to be included in a segment group. In particular, J can bebased on the redundancy storage encoding scheme utilized to generate theset of segments and/or to generate the corresponding parity data 2426.

The segment generator 2517 of a stream loader module 2510 can access thepage cache 2512 of the stream loader module 2510 to convert the pages2515 previously generated by the page generator 2511 into segments. Insome cases, each segment generator 2517 requires access to all pages2515 generated by the segment generator 2517 since the last conversionprocess of pages into segments. The page cache 2512 can optionally storeall pages generated by the page generator 2511 since the last conversionprocess, where the segment generator 2517 accesses all of these pagesgenerated since the last conversion process to cluster records intogroups and generate segments. For example, the page cache 2512 isimplemented as a write-through cache to enable all previously generatedpages since the last conversion process to be accessed by the segmentgenerator 2517 once the conversion process commences.

In some cases, each stream loader module 2510 implements its segmentgenerator 2517 upon only the set of pages 2515 that were generated byits own page generator 2511, accessible via its own page cache 2512. Insuch cases, the record grouping via clustering key to create segmentswith the same or similar cluster keys are separately performed by eachsegment generator 2517 independently without coordination, where thisrecord grouping via clustering key is performed on N distinct sets ofrecords stored in the N distinct sets of pages generated by the Ndistinct page generators 2511 of the N distinct stream loader modules2510. In such cases, despite records never being shared between streamloader modules 2510 to further improve clustering, the level ofclustering of the resulting segments generated independently by eachstream loader module 2510 on its own data is sufficient, for example,due to the number of records in each stream loader module's 2510 set ofpages 2515 for conversion being sufficiently large to attain favorablelevels of clustering.

In such embodiments, each stream loader modules 2510 can independentlyinitiate its own conversion process of pages 2515 into segments 2424 bywaiting as long as possible based on its own resource utilization, suchas memory availability of its page cache 2512. Different segmentgenerators 2517 of the different stream loader modules 2510 can thusperform their own conversion of the corresponding set of pages 2515 intosegments 2424 at different times, based on when each stream loadermodules 2510 independently determines to initiate the conversionprocess, for example, based on each independently making thedetermination to generate segments as discussed in conjunction with FIG.26A. Thus, as discussed herein, the conversion process of pages intosegments can correspond to a single stream loader module 2510 convertingall of its pages 2515 generated by its own page generator 2511 since itsown last the conversion process into segments 2424, where differentstream loader modules 2510 can initiate and execute this conversionprocess at different times and/or with different frequency.

In other cases, it is ideal for even more favorable levels of clusteringto be attained via sharing of all pages for conversion across all streamloader modules 2510. In such cases, a collective decision to initiatethe conversion process can be made across some or all stream loadermodules 2510, for example, based on resource utilization across allstream loader modules 2510. The conversion process can include sharingof and/or access to all pages 2515 generated via the process, where eachsegment generator 2517 accesses records in some or all pages 2515generated by and/or stored by some or all other stream loader modules2510 to perform the record grouping by cluster key. As the full set ofrecords is utilized for this clustering instead of N distinct sets ofrecords, the levels of clustering in resulting segments can be furtherimproved in such embodiments. This improved level of clustering canoffset the increased page movement and coordination required tofacilitate page access across multiple stream loader modules 2510. Asdiscussed herein, the conversion process of pages into segments canoptionally correspond to multiple stream loader modules 2510 convertingall of their collectively generated pages 2515 since their lastconversion process into segments 2424 via sharing of their generatedpages 2515.

An index generator 2513 can optionally be implemented by some or allstream loader modules 2510 to generate index data 2516 for some or allpages 2515 prior to their conversion into segments. The index data 2516generated for a given page 2515 can be appended to the given page, canbe stored as metadata of the given page 2515, and/or can otherwise bemapped to the given page 2515. The index data 2516 for a given page 2515correspond to page metadata, for example, indexing records included inthe corresponding page. As a particular example, the index data 2516 caninclude some or all of the data of index data 2518 generated forsegments 2424 as discussed previously, such as index sections 0-x ofFIG. 23 . As another example, the index data 2516 can include indexinginformation utilized to determine the memory location of particularrecords and/or particular columns within the corresponding page 2515.

In some cases, the index data 2516 can be generated to enablecorresponding pages 2515 to be processed by query IO operators utilizedto read rows from pages, for example, in a same or similar fashion asindex data 2518 is utilized to read rows from segments. In some cases,index probing operations can be utilized by and/or integrated withinquery IO operators to filter the set of rows returned in reading a page2515 based on its index data 2516 and/or to filter the set of rowsreturned in reading a segment 2424 based on its index data 2518.

In some cases, index data 2516 is generated by index generator 2513 forall pages 2515, for example, as each page 2515 is generated, or at somepoint after each page 2515 is generated. In other cases, index data 2516is only generated for some pages 2515, for example, where some pages donot have index data 2516 as illustrated in FIG. 25B. For example, somepages 2515 may never have corresponding index data 2516 generated priorto their conversion into segments. In some cases, index data 2516 isgenerated for a given page 2515 with its records are to be read inexecution of a query by the query processing system 2502. For example, anode 37 at IO level 2416 can be implemented as a stream loader module2510 and can utilize its index generator 2513 to generate index data2516 for a particular page 2515 in response to having query executionplan data indicating that records 2422 be read the particular page fromthe page cache 2512 of the stream loader module in conjunction withexecution of a query. The index data 2516 can be optionally storedtemporarily for the life of the given query to facilitate reading ofrows from the corresponding page for the given query only. The indexdata 2516 alternatively be stored as metadata of the page 2515 oncegenerated, as illustrated in FIG. 25B. This enables the previouslygenerated index data 2516 of a given page to be utilized in subsequentqueries requiring reads from the given page.

As illustrated in FIG. 25B, each stream loader modules 2510 can generateand send pages 2515, corresponding index data 2516, and/or segments 2424to long term storage 2540-1-2540-J of a particular storage cluster 2535.For example, system communication resources 14 can be utilized tofacilitate sending of data from stream loader modules 2510 to storagecluster 2535 and/or to facilitate sending of data from storage cluster2535 to stream loader modules 2510.

The storage cluster 2535 can be implemented by utilizing a storagecluster 35 of FIG. 6 , where each long term storage 2540-1-2540-J isimplemented by a corresponding computing device 18-1-18-J and/or by acorresponding node 37-1-37-J. In some cases, each storage cluster35-1-35-z of FIG. 6 can receive pages 2515, corresponding index data2516, and/or segments 2424 from its own set of stream loader modules2510-1-2510-N, where the record processing and storage system 2505 ofFIG. 25B can include z sets of stream loader modules 2510-1-2510-N thateach generate pages 2515, segments 2524, and/or index data 2516 forstorage in its own corresponding storage cluster 35. Embodiments ofimplementing a record processing and storage system 2505 via multiplestorage clusters 35 is discussed in further detail in conjunction withFIGS. 30A-30C.

The processing and/or memory resources utilized to implement each longterm storage 2540 can be distinct from the processing and/or memoryresources utilized to implement the stream loader modules 2510.Alternatively, some stream loader modules can optionally shareprocessing and/or memory resources long term storage 2540, for example,where a same computing device 18 and/or a same node 37 implements aparticular long term storage 2540 and also implements a particularstream loader modules 2510.

Each stream loader module 2510 can generate and send the segments 2424to long term storage 2540-1-2540-J in a set of persistence batches2532-1-2532-J sent to the set of long term storage 2540-1-2540-J asillustrated in FIG. 25B. For example, upon generating a segment group2522 of J segments 2424, a stream loader module 2510 can send each ofthe J segments in the same segment group to a different one of the setof long term storage 2540-1-2540-J in the storage cluster 2535. Forexample, a particular long term storage 2540 can generate recoveredsegments as necessary for processing queries and/or for rebuildingmissing segments due to drive failure as illustrated in FIG. 24D, wherethe value K of FIG. 24D is less than the value J and wherein the nodes37 of FIG. 24D are utilized to implement the long term storage2540-1-2540-J.

As illustrated in FIG. 25B, each persistence batch 2532-1-2532-J canoptionally or additionally include pages 2515 and/or their correspondingindex data 2516 generated via index generator 2513. Some or all pages2515 that are generated via a stream loader module 2510's page generator2511 can be sent to one or more long term storage 2540-1-2540-J. Forexample, a particular page 2515 can be included in some or allpersistence batches 2532-1-2532-J sent to multiple ones of the set oflong term storage 2540-1-2540-J for redundancy storage as replicatedpages stored in multiple locations for the purpose of fault tolerance.Some or all pages 2515 can be sent to storage cluster 2535 for storageprior to being converted into segments 2424 via segment generator 2517.Some or all pages 2515 can be stored by storage cluster 2535 untilcorresponding segments 2424 are generated, where storage cluster 2535facilitates deletion of these pages from storage in one or more longterm storage 2540-1-2540-J once these pages are converted and/or havetheir records 2422 successfully stored by storage cluster 2535 insegments 2424.

In some cases, a stream loader module 2510 maintains storage of pages2515 via page cache 2512, even if they are sent to storage cluster 2535in persistence batches 2532. This can enable the segment generator 2517to efficiently read pages 2515 during the conversion process via readsfrom this local page cache 2512. This can be ideal in minimizing pagemovement, as pages do not need to be retrieved from long term storage2540 for conversion into segments by stream loader modules 2510 and caninstead be locally accessed via maintained storage in page cache 2512.Alternatively, a stream loader module 2510 removes pages 2515 fromstorage via page cache 2512 once they are determined to be successfullystored in long term storage 2540. This can be ideal in reducing thememory resources required by stream loader module 2510 to store pages,as only pages that are not yet durably stored in long term storage 2540need be stored in page cache 2512.

Each long term storage 2540 can include its own page storage 2546 thatstores received pages 2515 generated by and received from one or morestream loader modules 2010-1-2010-N, implemented utilizing memoryresources of the long term storage 2540. For example, the page storage2546 of each long term storage 2540-1-2540-J can individually orcollectively implement some or all of the page storage system 2506 ofFIG. 25A. The page storage 2546 can optionally store index data 2516mapped to and/or included as metadata of its pages 2515. Each long termstorage 2540 can alternatively or additionally include its own segmentstorage 2548 that stores segments generated by and received from one ormore stream loader modules 2010-1-2010-N. For example, the segmentstorage 2548 of each long term storage 2540-1-2540-J can individually orcollectively implement some or all of the segment storage system 2508 ofFIG. 25A.

The pages 2515 stored in page storage 2546 of long term storage 2540and/or the segments 2424 stored in segment storage 2548 of long termstorage 2540 can be accessed to facilitate execution of queries. Asillustrated in FIG. 25B, each long term storage 2540-1-2540-J canperform IO operators 2542 to facilitate reads of records in pages 2515stored in their page storage 2546 and/or to facilitate reads of recordsin segments 2424 stored in their segment storage 2548. For example, someor all long term storage 2540-1-2540-J can be implemented as nodes 37 atthe IO level 2416 of one or more query execution plans 2405. Inparticular, the some or all long term storage 2540-1-2540-J can beutilized to implement the query processing system 2502 by facilitatingreads to stored records via IO operators 2542 in conjunction with queryexecutions.

Note that at a given time, a given page 2515 may be stored in the pagecache 2512 of the stream loader module 2510 that generated the givenpage 2515, and may alternatively or additionally be stored in one ormore long term storage 2540 of the storage cluster 2535 based on beingsent to the in one or more long term storage 2540. Furthermore, at agiven time, a given record may be stored in a particular page 2515 in apage cache 2512 of a stream loader module 2510, may be stored theparticular page 2515 in page storage 2546 of one or more long termstorage 2540, and/or may be stored in exactly one particular segment2424 in segment storage 2548 of one long term storage 2540.

Because records can be stored in multiple locations of storage cluster2535, the long term storage 2540 of storage cluster 2535 can be operableto collectively store page and segment ownership consensus 2544. Thiscan be useful in dictating which long term storage 2540 is responsiblefor accessing each given record stored by the storage cluster 2535 viaIO operators 2542 in conjunction with query execution. In particular, asa query resultant is only guaranteed to be correct if each requiredrecord is accessed exactly once, records reads to a particular recordstored in multiple locations could render a query resultant asincorrect. The page and segment ownership consensus 2544 can include oneor more versions of ownership data, for example, that is generated viaexecution of a consensus protocol mediated via the set of long termstorage 2540-1-2540-J. The page and segment ownership consensus 2544 candictate that every record is owned by exactly one long term storage 2540via access to either a page 2515 storing the record or a segment 2424storing the record, but not both. The page and segment ownershipconsensus 2544 can indicate, for each long term storage 2540 in thestorage cluster 2535, whether some or all of its pages 2515 or some orall of its segments 2424 are to be accessed in query executions, whereeach long term storage 2540 only accesses the pages 2515 and segments2424 indicated in page and segment ownership consensus 2544.

In such cases, all record access for query executions performed by queryexecution module 2504 via nodes 37 at IO level 2416 can optionally beperformed via IO operators 2542 accessing page storage 2546 and/orsegment storage 2548 of long term storage 2540, as this access canguarantee reading of records exactly once via the page and segmentownership consensus 2544. For example, the long term storage 2540 can besolely responsible for durably storing the records utilized in queryexecutions. In such embodiments, the cached and/or temporary storage ofpages and/or segments of stream loader modules 2510, such as pages 2515in page caches 2512, are not read for query executions via accesses tostorage resources of stream loader modules 2510.

FIG. 25C illustrates an example embodiment of a page generator 2511. Thepage generator 2511 of FIG. 25C can be utilized to implement the pagegenerator 2511 of FIG. 25A, can be utilized to implement each pagegenerator 2511 of each stream loader module 2510 of FIG. 25B, and/or canbe utilized to implement any embodiments of page generator 2511described herein. An example embodiment of the page generator 2511 ofFIG. 25C is discussed in further detail in conjunction with FIGS.31A-31F.

A single incoming record stream, or multiple incoming record streams1-L, can include the incoming records 2422 as a stream of row data 2910.Each row data 2910 can be transmitted as an individual packet and/or aset of packets by the corresponding data source 2501 to include a singlerecord 2422, such as a single row of a database table. Alternativelyeach row data 2910 can transmitted by the corresponding data source 2501as an individual packet and/or a set of packets to include a batched setof multiple records 2422, such as multiple rows of a database table. Rowdata 2910 received from the same or different data source over time caneach include a same number of rows or a different number of rows, andcan be sent in accordance with a particular format. Row data 2910received from the same or different data source over time can includerecords with the same or different numbers of columns, with the same ordifferent types and/or sizes of data populating its columns, and/or withthe same or different row schemas. In some cases, row data 2910 isreceived in a stream over time for processing by a stream loader module2510 via a stateful file reader 2556 and/or via a stand-alone filereader 2558.

Incoming rows can be stored in a pending row data pool 3410 while theyawait conversion into pages 2515. The pending row data pool 3410 can beimplemented as an ordered queue or an unordered set. The pending rowdata pool 3410 can be implemented by utilizing storage resources of therecord processing and storage system. For example, each stream loadermodule 2510 can have its own pending row data pool 3410. Alternatively,multiple stream loader modules 2510 can access the same pending row datapool 3410 that stores all incoming row data 2910, for example, byutilizing queue reader 2559.

The page generator 2511 can facilitate parallelized page generation viaa plurality of processing core resources 48-1-48-W. For example, eachstream loader module 2510 has its own plurality of processing coreresources 48-1-48-W, where the processing core resources 48-1-48-W of agiven stream loader module 2510 is implemented via the set of processingcore resources 48 of one or more nodes 37 utilized to implement thegiven stream loader module 2510. As another example, the plurality ofprocessing core resources 48-1-48-W are each implemented by acorresponding one of the set of each stream loader module 2510-1-2510-N,for example, where each stream loader module 2510-1-2510-N isimplemented via its own processing core resources 48-1-48-W.

Over time, each processing core resource 48 can retrieve and/or can beassigned pending row data 2910 in the pending row data pool 3410. Forexample, when a given processing core resource 48 has finished anotherjob, such as completed processing of another row data 2910, theprocessing core resource 48 can fetch a new row data 2910 for processinginto a page 2515. For example, the processing core resource 48 retrievesa first ordered row data 2910 from a queue of the pending row data pool3410, retrieves a highest priority row data 2910 from the pending rowdata pool 3410, retrieves an oldest row data 2910 from the pending rowdata pool 3410, and/or retrieves a random row data 2910 from the pendingrow data pool 3410. Once one processing core resource 48 retrievesand/or otherwise utilizes a particular row data 2910 for processing intoa page, the particular row data 2910 is removed from the pending rowdata pool 3410 and/or is otherwise not available for processing by otherprocessing core resources 48.

Each processing core resource 48 can generate pages 2515 from the rowdata received over time. As illustrated in FIG. 25C, the pages 2515 aredepicted to include only one row data, such as a single row or multiplerows batched together in the row data 2910. For example, each page isgenerated directly from corresponding row data 2910. Alternatively, apage 2515 can include multiple row data 2910, for example, in sequenceand/or concatenated in the page 2515. The page can include multiple rowdata 2910 from a single data source 2501 and/or can include multiple rowdata 2910 from multiple different data sources 2501. For example, theprocessing core resource 48 can retrieve one row data 2910 from thepending row data pool 3410 at a time, and can append each row data 2910to a given page until the page 2515 is complete, where the processingcore resource 48 appends subsequently retrieved row data 2910 to a newpage. Alternatively, the processing core resource 48 can retrievemultiple row data 2910 at once, and can generate a corresponding page2515 to include this set of multiple row data 2910.

Once a page 2515 is complete, the corresponding processing core resource48 can facilitate storage of the page in page storage system 2506. Thiscan include adding the page 2515 to the page cache 2512 of thecorresponding stream loader module 2510. This can include facilitatingsending of the page 2515 to one or more long term storage 2540 forstorage in corresponding page storage 2546. Different processing coreresources 48 can each facilitate storage of the page via commonresources, or via designated resources specific to each processing coreresources 48, of the page storage system 2506.

FIG. 25D illustrates an example embodiment of the page storage system2605. As used herein, the page storage system 2605 can include pagecache 2512 of a single stream loader module 2510; can include pagecaches 2512 of some or all stream loader module 2510-1-2510-N; caninclude page storage 2546 of a single long term storage 2540 of astorage cluster 2535; can include page storage 2546 of some or all longterm storage 2540-1-2540-J of a single storage cluster 2535; can includepage storage 2546 of some or all long term storage 2540-1-2540-J ofmultiple different storage clusters, such as some or all storageclusters 35-1-35-z; and/or can include any other memory resources ofdatabase system 10 that are utilized to temporarily and/or durably storepages.

FIG. 25E illustrates an example embodiment of a node 37 utilized toimplement a given long term storage 2540 of FIG. 25B. The node 37 ofFIG. 25E can be utilized to implement the node 37 of FIG. 25B, FIG. 25C,25D, some or all nodes 37 at the IO level 2416 of a query execution plan2405 of FIG. 24A, and/or any other embodiments of node 37 describedherein. As illustrated a given node 37 can have its own segment storage2548 and/or its own page storage 2546 by utilizing one or more of itsown memory drives 2425. Note that while the segment storage 2548 andpage storage 2546 are segregated in the depiction of a memory drives2425, any resources of a given memory drive or set of memory drives canbe allocated for and/or otherwise utilized to store either pages 2515 orsegments 2424. Optionally, some particular memory drives 2425 and/orparticular memory locations within a particular memory drive can bedesignated for storage of pages 2515, while other particular memorydrives 2425 and/or other particular memory locations within a particularmemory drive can be designated for storage of segments 2424.

The node 37 can utilize its query processing module 2435 to access pagesand/or records in conjunction with its role in a query execution plan2405, for example, at the IO level 2416. For example, the queryprocessing module 2435 generates and sends segment read requests toaccess records stored in segments of segment storage 2548, and/orgenerates and sends page read requests to access records stored in pages2515 of page storage 2546. In some cases, in executing a given query,the node 37 reads some records from segments 2424 and reads otherrecords from pages 2515, for example, based on assignment data indicatedin the page and segment ownership consensus 2544. The query processingmodule 2435 can generate its data blocks to include the raw row data ofthe read records and/or can perform other query operators to generateits output data blocks as discussed previously. The data blocks can besent to another node 37 in the query execution plan 2405 for processingas discussed previously, such as a parent node and/or a node in ashuffle node set within the same level 2410.

FIG. 26A illustrates an example embodiment of a segment generator 2517.The segment generator 2517 of FIG. 26A can be utilized to implement thesegment generator 2517 of FIG. 25A, can be utilized to implement eachsegment generator 2517 of each stream loader module 2510 of FIG. 25B,and/or can be utilized to implement any embodiments of segment generator2517 described herein.

As discussed previously, the record processing and storage system 2505can be operable to delay the conversion of pages into segments. Ratherthan frequently clustering rows and converting rows into column format,movement and/or processing of rows can be minimized by delaying theclustering and conversion process required to generate segments 2424,for example, as long as possible. This delaying of the conversionprocess “as long as possible” can be bounded by resource availability,such as disk and/or memory capacity of the record processing and storagesystem 2505. In particular, the conversion process can be delayed toaccumulate as many pages in the page storage system 2506 that pagestorage system 2506 is capable of storing.

Maximizing the delay until pages are processed as enabled by storageresources of the record processing and storage system 2505 improves thetechnology of database systems by improving query efficiency. Inparticular, delaying the decision of which rows to group together intosegments as long as possible increased the chances of having manyrecords with common cluster keys to group together, as cluster key-basedgroups are formed from a largest possible set of records. These morefavorable levels of clustering enable queries to be performed moreefficiently as discussed previously. For example, rows that need beaccessed in a given query as dictated by filtering parameters of thequery are more likely to be stored together, and fewer segments and/ormemory locations need to be accessed.

Maximizing the delay until pages are processed as enabled by storageresources of the record processing and storage system 2505 improves thetechnology of database systems by improving data ingress efficiency. Byplacing rows directly into pages without regard for clustering as theyare received, this delayed approach minimizes the number of times a row“moves” through the system, such as from disk, to memory, and/or throughthe processor. In particular, by delaying all clustering until segmentgeneration for the received rows all at once, the rows are moved exactlyonce, to their final resting place as a segment 2424. This conservesresources of the record processing and storage system 2505, enablinghigher rates of records to be received and processed for storage viadata sources 2501 and thus enabling a richer, denser database to begenerated over time. For example, this can enable the record processingand storage system 2505 to effectively process incoming records at ascale of terabits per second.

This delay can be accomplished via a page conversion determinationmodule 2610 implemented by the segment generator 2517 and/or implementedvia other processing resources of the record processing and storagesystem 2505. The page conversion determination module 2610 can beutilized to generate segment generation determination data indicatingwhether the conversion process of pages into segments should becommenced at a given time. For example, the page conversiondetermination module 2610 generates an interrupt or notification thatincludes the generate segment generation determination data indicatingit is time to generate segments based on determining to generatesegments at the given time. The page conversion determination module2610 can otherwise trigger the commencement of converting pages intosegments once it deems the conversion process appropriate, for example,based on delaying as long as possible. The segment generator 2517 cancommence the conversion process accordingly in response to the segmentgeneration determination data indicating it is time to generatesegments, for example, via a cluster key-based grouping module 2620, acolumnar rotation module 2630, and/or a metadata generator module 2640.The delay of converting pages into segments via the page conversiondetermination module 2610 and the repeating of this process over time isdiscussed in further detail in conjunction with the example timeline ofFIG. 26B.

In some cases, the page conversion determination module 2610 optionallygenerates some segment generation determination data indicating it isnot yet time to generate segments. In some embodiments, this informationmay not be communicated if it is determined that is not yet time togenerate segments, where only notifications instructing the conversionprocess be commenced is communicated to initiate the process via clusterkey-based grouping module 2620, a columnar rotation module 2630, and/ora metadata generator module 2640.

The page conversion determination module 2610 can generate segmentgeneration determination data: in predetermined intervals; in accordancewith a schedule; in response to determining a new page has beengenerated and stored in page storage system 2506; in responsedetermining at least a threshold number of new pages have been generatedand stored in page storage system 2506; in response to determining thestorage space and/or memory utilization of page storage system 2506 haschanged; in response to determining the total storage capacity of pagestorage system 2506 has changed; in response to determining at least onememory drive of the page storage system 2506 has failed or gone offline;in response to receiving storage utilization data from page storagesystem 2506; based on instruction supplied via user input, for example,via administration sub-system 15 and/or configuration sub-system 16;based on receiving a request; and/or based on another determination.

The page conversion determination module 2610 can generate its segmentgeneration determination data based on comparing storage utilizationdata 2606 to predetermined conversion threshold data 2605. The storageutilization data can optionally be generated by the page storage system2506. The record processing and storage system 2505 can indicate and/orbe based on one or more storage utilization metrics indicating: anamount and/or percentage of storage resources of the page storage system2506 that are currently being utilized to store pages 2515; an amountand/or percentage of available resources of the page storage system 2506that are not currently being utilized to store pages 2515; a number ofpages 2515 currently stored by the page storage system 2506; a datasize, such as a number of bytes, of the set of pages 2515 currentlystored by the page storage system 2506; an expected amount of time untilstorage resources of the page storage system 2506 are expected to becomefully utilized for page storage based on current and/or historical datarates of record streams 1-L; current health data and/or failure data ofstorage resources of the page storage system 2506; an amount of timesince the last conversion process was initiated and/or was completed;and/or other information regarding the storage utilization of the pagestorage system 2506.

In some cases, the storage utilization data 2606 can relate specificallyto storage utilization of a page cache 2512 of a stream loader module2510 of FIG. 25B, where the segment generator 2517 of FIG. 26A isimplemented by the corresponding stream loader module 2510 and where thesegment generator 2517 of FIG. 26A is operable to perform the conversionprocess only upon pages 2515 in the page cache 2512. In some cases, thestorage utilization data 2606 can relate specifically to storageutilization across all page caches 2512 of all stream loader modules2510-1-2510-N, where the page conversion determination module 2610 ofFIG. 26A is implemented to dictate whether the conversion process becommenced across all corresponding stream loader modules 2510. In somecases, the storage utilization data 2606 can alternatively or include tostorage utilization of page storage 2546 of one or more of the long termstorage 2540-1-2540-J of FIG. 25B. The storage utilization data 2606 canrelate to any combination of storage resources of page storage system2506 as discussed in conjunction with FIG. 25D that are utilized tostore a particular set of pages to be converted into segments in tandemvia the conversion process performed by segment generator 2517.

The storage utilization data 2606 can be sent to and/or requested by thesegment generator 2517: in predefined intervals; in accordance withscheduling data; based on the page conversion determination module 2610determining to generate the segment generation determination data; basedon a determination, notification, and/or instruction that the pageconversion determination module 2610 should generate the segmentgeneration determination data; and/or based on another determination. Insome cases, some or all of the page conversion determination module 2610is implemented via processing resources and/or memory resources of thepage storage system 2506, for example, to enable the page conversiondetermination module 2610 to monitor and/or measure the storageutilization data 2606 of its own resources included in page storagesystem 2506.

The predetermined conversion threshold data 2605 can indicate one ormore threshold metrics or other threshold conditions that, when met byone or more corresponding metrics of the storage utilization data 2606at a given time, trigger the commencement of the conversion process. Inparticular, the page conversion determination module generates thesegment generation determination data indicating that segments begenerated when the at least one metric of the storage utilization data2606 meets the threshold metrics and/or conditions of the predeterminedconversion threshold data 2605 and/or otherwise compares favorably to acondition for page conversion indicated by the predetermined conversionthreshold data 2605. If the none of the metrics of the storageutilization data 2606 compare favorably to corresponding thresholdmetrics of predetermined conversion threshold data 2605, the pageconversion determination module generates the segment generationdetermination data indicating that segments not be generated at thistime, or otherwise does not generate the segment generationdetermination data in this case as no instruction to commence conversionneed be communicated.

In some cases, the page conversion determination module generates thesegment generation determination data indicating that segments begenerated only when at least a predetermined threshold number of metricsof the storage utilization data 2606 compare favorably to thecorresponding threshold metrics of the predetermined conversionthreshold data 2605. In such cases, if less than the predeterminedthreshold number of metrics of the storage utilization data 2606 comparefavorably to corresponding threshold metrics of predetermined conversionthreshold data 2605, the page conversion determination module generatesthe segment generation determination data indicating that segments notbe generated at this time, or otherwise does not generate the segmentgeneration determination data in this case as no instruction to commenceconversion need be communicated.

In some cases, there is only one metric in the storage utilization data2606 that is compared to a corresponding metric of the predeterminedconversion threshold data 2605, and the page conversion determinationmodule generates the segment generation determination data when themetric in the storage utilization data 2606 meets or otherwise comparesfavorably to the corresponding metric of the predetermined conversionthreshold data 2605.

As used herein, the storage utilization data 2606 compares favorably tothe predetermined conversion threshold data 2605 when the conditionsindicated in the predetermined conversion threshold data 2605 thatdictate the conversion process be initiated are met by correspondingmetrics of the storage utilization data 2606. As used herein, thestorage utilization data 2606 compares unfavorably to the predeterminedconversion threshold data 2605 when the conditions indicated in thepredetermined conversion threshold data 2605 that dictate the conversionprocess be initiated are not met by corresponding metrics of the storageutilization data 2606. In some embodiments, the page conversiondetermination module 2610 generates the segment generation determinationdata indicating that segments be generated and/or otherwise indicatingthat the conversion process be initiated only when the storageutilization data 2606 compares favorably to the predetermined conversionthreshold data 2605.

The predetermined conversion threshold data 2605 can indicate one ormore conditions that trigger the conversion process such as: a totalmemory capacity of page storage system 2506; a threshold maximum amountand/or percentage of storage resources of the page storage system 2506that can be utilized to store pages 2515; a threshold minimum amountand/or percentage of resources page storage system that must remainavailable; a threshold minimum number of pages 2515 that must beincluded in the set of pages for conversion; a threshold maximum numberof pages 2515 that can be converted in a single conversion process; athreshold maximum and/or threshold a data size of the set of pages thatcan be converted in a single conversion process; a threshold minimumamount of time that storage resources of the page storage system can beexpected to become fully utilized for page storage based on currentand/or historical data rates of record streams 1-L; thresholdrequirements for health data and/or failure data of storage resources ofthe page storage system 2506; a threshold minimum and/or thresholdmaximum amount of time at which a new conversion process must commencesince the last conversion process was initiated and/or was completed;and/or other information regarding the requirements and/or conditionsfor initiation of the conversion process.

The predetermined conversion threshold data 2605 can be received and/orconfigured based on user input, for example, via administrativesub-system 15 and/or via configuration sub-system 16. The predeterminedconversion threshold data 2605 can alternatively or additionally bedetermined automatically by the record processing and storage system2505. For example, the predetermined conversion threshold data 2605 canbe determined automatically to indicate and/or be based on determining athreshold memory capacity of the page storage system 2506; based ondetermining a threshold amount of bytes worth of pages 2515 the pagestorage system 2506 can store; and/or based on determining a thresholdexpected and/or average amount of time that pages can be generated andstored in the page storage system 2506 by the page generator 2511 untilthe page storage system 2506 becomes full. Note that these thresholdscan be automatically buffered to account for a threshold percentage ofdrive failures, a historical expected rate of drive failures, athreshold amount of additional pages data that may be stored incommunication lag since the storage utilization data 2606 was sent, athreshold amount of additional pages data that may be stored inprocessing lag to perform some or all of the conversion process, and/orother buffering to ensure that segment generation is completed beforepage storage system 2506 reaches its capacity.

As another example, the predetermined conversion threshold data 2605 canbe determined automatically based on determining a sufficient number ofrecords 2422 and/or a sufficient number of pages 2515 that can achievesufficiently favorable levels of clustering. For example, this can bebased on tracking and/or measuring clustering metrics for records inprevious iterations of the conversion process and/or based on analysisof the measuring clustering metrics for records in previous iterationsof the process to determine and/or estimate these thresholds. Thestorage utilization data 2606 can also be measured and/or tracked foreach of this plurality of previous conversion processes to determineaverage and/or estimated storage utilization metrics that renderedconversion processes with favorable levels of clustering based on thecorresponding clustering metrics measured for these previous conversionprocesses.

The clustering metrics can be based on a total or average number and/orproportion of records in each segment that: match cluster key of atleast a threshold proportion of other records in the segment, are withina threshold vector distance and/or other similarity measure from atleast a threshold number of other records in the segment. The clusteringmetrics can alternatively or additionally be based on an average and/ortotal number of segments whose records have a variance and/or standarddeviation of their cluster key values that compare favorably to athreshold. The clustering metrics can alternatively or additionally bedetermined in accordance with any other similarity metrics and/orclustering algorithms.

Once the page conversion determination module 2610 generates segmentgeneration determination data indicating that segments be generated viathe conversion process, the segment generator 2517 can initiate theprocess of generating stored pages into segments. This can includeidentifying the pages for conversion in the conversion process. Forexample, all pages currently stored by the page storage system 2506 andawaiting their conversion into segments 2424 at the time when segmentgeneration determination data is generated to indicating that theconversion process commence are identified for conversion. This set ofpages can constitute a conversion page set 2655, where only the set ofpages identified for conversion in the conversion page set 2655 areprocessed by segment generator 2517 for a given conversion process. Forexample, the record processing and storage system 2505 may continue toreceive records from data sources 2501, and rather than buffering all ofthese records until after this conversion process is completed,additional pages can be generated at this time for storage in pagestorage system 2506. However, as processing of pages into segments hasalready commenced, these pages may not be clustered and converted duringthis conversion process, and can await their conversion in the nextiteration of the conversion process. As another example, the pagestorage system 2506 may still be storing some other pages that werepreviously converted into segments but were not yet deleted. These pagesare similarly not included in the conversion page set 2655 because theirrecords are already included in segments via the prior conversion.

The segment generator can implement a cluster key-based grouping module2620 to generate a plurality of record groups 2625-1-2625-X from theplurality of records 2422 included in the conversion page set 2655. Thecluster key-based grouping module 2620 can receive and/or determine acluster key 2607, which can be automatically determined by the clusterkey-based grouping module 2620, can be stored in memory, can be receivedfrom another computing device, and/or can be configured via user input.The cluster key can indicate one or more columns, such as the keycolumn(s) of FIGS. 18-22 , by which the records are to be sorted andsegregated into the record groups. For example, the plurality of records2422 included in the conversion page set 2655 are sorted and/or groupedby cluster key, where records 2422 with matching cluster keys and/orsimilar cluster keys are grouped together in the resulting record groups2625-1-2625-X. The record groups 2625-1-2625-X can be a fixed size, orcan be dynamic in size, for example, based on including only recordsthat have matching and/or similar cluster keys. An example of generatingthe record groups 2625-1-2625-X via the cluster key-based groupingmodule 2620 is illustrated in FIG. 26C.

The records 2422 of each record group in the set of record groups2625-1-2625-X generated by the cluster key-based grouping module 2620are ultimately included in one segment 2424 of a corresponding segmentgroup in the set of segment groups 1-X generated by the segmentgenerator 1-X. For example, segment group 1 includes a set of segments2424-1-2424-J that include the records 2422 from record groups 2625-1,segment group 2 includes another set of segments 2424-1-2424-J thatinclude the records 2422 from record groups 2625-2, and so on. Theidentified record groups 2625-1-2625-X can be converted into segments ina same or similar fashion as discussed in conjunction with FIGS. 18-23 .

The record groups are processed into segments via a columnar rotationmodule 2630 of the segment generator 2517. Once the plurality of recordgroups 2625-1-2625-X are formed, the columnar rotation module 2630 canbe implemented to generate column-formatted record data 2565 for eachrecord group 2625. For example, the records 2422 of each record groupare extracted from pages 2515 as row-formatted data. In particular, therecords 2422 can be received from data sources 2501 as row-formatteddata and/or can be stored in pages 2515 as row-formatted data. Allrecords 2422 in the same record group 2625 are converted intocolumn-formatted row data 2565 in accordance with a column-based format,for example, by performing a columnar rotation of the row-formatted dataof the records 2422 in the given record group 2625. The column-formattedrow data 2565 generated for a given record group 2625 can be dividedinto a set of column-formatted row data 2565-1-2565-J, for example,where the column-formatted row data 2565 is redundancy storage errorencoded by the segment generator 2517 as discussed previously, and whereeach column-formatted row data 2565-1-2565-J is included in acorresponding segment of a set of J segments 2424 of a segment group2522.

The final segments can be formed from the column-formatted row data 2565to include metadata generated via a metadata generator module 2640. Themetadata generator module 2640 can be operable to generate the manifestsection, statistics section, and/or the set of index sections 0-x foreach segment as illustrated in FIG. 23 . The metadata generator module2640 can generate the index data 2518 for each segment 2424 by utilizingthe same or different index generator 2513 of FIG. 25B, where index data2518 generated for segments 2424 via the metadata generator module 2640is the same as or similar to the index data 2516 generated for pages asdiscussed in conjunction with FIG. 25B. The column-formatted row data2565 and its metadata generated via metadata generator module 2640 canbe combined to form a final corresponding segment 2424.

FIG. 26B depicts an example timeline illustrating when the conversionprocess is determined to be conducted and how this process is iteratedover time. The page conversion determination module 2610, and/or thedeterminations to delay conversion versus initiate conversion over timeas illustrated in FIG. 26B, can be utilized to implement the segmentgenerator 2517 of FIG. 26A and/or any other embodiment of the segmentgenerator 2517 discussed herein.

First, a first conversion page set 2655-1 accumulates pages 2515 overtime until the page conversion determination module 2610 determines aconversion page set 2655-1 is ready for conversion. At time t₁, theconversion page set 2655-1 includes a small number of pages 2515, wherethe storage resources of page storage system 2506 are not yet fullyutilized. This small number of pages relative to the page storagecapacity of page storage system 2506 renders the storage utilizationdata 2606 at time t₁ to compare unfavorably to the predeterminedconversion threshold data. The segment generation determination datagenerated by the page conversion determination module 2610 at time t₁therefore delays the conversion process, indicating to wait for morepages 2515 rather than generating segments from the current conversionpage set 2655-1 at time t₁.

At time t₂, more pages 2515 have been accumulated since time t₁ based onadditional pages having been generated by the page generator 2511 fromincoming records of one or more record streams. However, the storageresources of page storage system 2506 are still not yet fully utilizedat this time, causing the storage utilization data 2606 at time t₂ toagain compare unfavorably to the predetermined conversion thresholddata. The segment generation determination data generated by the pageconversion determination module 2610 at time t₂ again delays theconversion process, indicating to wait for more pages 2515 rather thangenerating segments from the current conversion page set 2655-1 at timet₂.

At time t₃, even more pages 2515 have been accumulated since time t₂,and storage resources of page storage system 2506 are fully utilizedand/or sufficiently utilized as dictated by the predetermined conversionthreshold data. Thus, enough pages have been accumulated to causestorage utilization data 2606 at time t₃ to compare favorably to thepredetermined conversion threshold data. The segment generationdetermination data generated by the page conversion determination module2610 at time t₃ initiates the conversion process by indicating thatsegments be generated from the current conversion page set 2655-1 attime t₃.

After time t₃, the pages of the conversion page set 2655-1 can beflushed to other storage and/or can be removed from page storage system2506. For example, once the segments are successfully generated fromconversion page set 2655-1, the pages of conversion page set 2655-1 aredeleted from page storage system 2506. The storage utilization data 2606can indicate that more pages be accumulated for the a next conversionpage set 2655-2, for example, due to the storage resources of pagestorage system 2506 again becoming available for storing new pages oncethe pages of conversion page set 2655-1 are removed.

At time t₄, after some or all of the pages of conversion page set 2655-1have been removed from storage by page storage system 2506, new pageshave been generated and stored in page storage system 2506 forconversion in the next conversion page set 2655-2. For example, the nextconversion page set 2655-2 can include some pages that were generatedwhile the conversion process of conversion page set 2655-2 was inprogress and/or while the resulting segments were being stored in tosegment storage system 2508. At this time, the storage resources of pagestorage system 2506 are not yet fully utilized at this time, causing thestorage utilization data 2606 at time t₄ to compare unfavorably to thepredetermined conversion threshold data.

At some later time after t₄, enough pages are accumulated in this nextconversion page set 2655-2 to cause the storage utilization data 2606 attime t₄ to compare favorably to the predetermined conversion thresholddata and to initiate another conversion process of converting theconversion page set 2655-2 into segments. This process can continueaccumulating and converting subsequent conversion page sets 2655 overtime.

Note that the predetermined conversion threshold data can change overtime, for example, based on different user configurations, based onchanges to storage capacity of the page storage system 2506, based onadding or removal of memory devices of page storage system 2506, basedon failures of page storage system 2506, based on trends in clusteringlevels that can be attained by different numbers of pages at differenttimes, based on changes in amount of different data stored by theresources of the page storage system 2506, based on resource assignmentchanges in the record processing and storage system 2505, and/or basedon other determinations made over time causing the predeterminedconversion threshold data to be adjusted accordingly. For example, thepredetermined conversion threshold data that triggers initiation of theconversion process for conversion page set 2655-1 at time t₃ can be thesame as or different from the predetermined conversion threshold datathat eventually triggers initiation of the conversion process forconversion page set 2655-2 at some later time after t₄.

FIG. 26C illustrates an example embodiment of a cluster key-basedgrouping module 2620 implemented by segment generator 2517. This exampleserves to illustrate that the grouping of sets of records in pages doesnot necessarily correlate with the sets of records in the record groupsgenerated by the cluster key-based grouping module 2620. In particular,in embodiments where the pages can be generated directly from sets ofincoming records as they arrive without any initial clustering, thegrouping of sets of records in pages may have no bearing on the recordgroups generated by the cluster key-based grouping module 2620 due tothe timestamp and/or receipt time of various records not necessarilyhaving a correlation with cluster key. The embodiment of clusterkey-based grouping module 2620 of FIG. 26C can be utilized to implementthe segment generator 2517 of FIG. 26A and/or any other embodiment ofthe segment generator 2517 discussed herein.

In this example, a plurality of P pages 2515-1-2515-P of conversion pageset 2655 include records received from one or more sources over time upuntil the page conversion determination module 2610 dictated thatconversion of this conversion page set 2655 commence. The plurality ofrecords in pages 2515-1-2515-P can be considered an unordered set ofpages to be clustered into record groups. Regardless of which pagesthese records may belong to, records are grouped into their recordgroups in accordance with cluster key. In this example, records of page2515-1 are dispersed across at least record groups 1 and 2; records ofpage 2515-2 are dispersed across at least record groups 1, 2, and X, andrecords of page 2515-P are dispersed across at least record groups 2 andX.

The value of X can be: predetermined prior to clustering, can be thesame or different for different conversion page sets 2655; can bedetermined based on a predetermined minimum and/or maximum number ofrecords that are included per record group; can be determined based on apredetermined minimum and/or maximum data size per record group; can bedetermined based on each record group having a predetermined level ofclustering, for example, in accordance with at least one clusteringmetric, and/or can be determined based on other information. In somecases, different record groups of the set of record groups 1-X caninclude different numbers of records, for example, based on maximizing aclustering metric across each record group.

For example, all records with a matching cluster key, such as having oneor more columns corresponding to the cluster key with matching values,can be included in a same record group. As another example, a set ofrecords having similar cluster keys can all be included in a same recordgroup. As another example, if the value of the cluster key can berepresented as a continuous variable, numeric variable, or othervariable with an inherent ordering with respect to a cluster key domain,the cluster key domain can be subdivided into a plurality of discreteintervals. In such cases, a given record group, or a given set of recordgroups, can include records with cluster keys having values in the samediscrete interval of the cluster key domain. As another example, arecord group has cluster key values that are within a predefineddistance from, or otherwise compare favorably to, an average cluster keyvalue of cluster keys within the record group. In such cases, aEuclidian distance metric, another vector distance metric, and/or anyother similarity and/or distance metric can be utilized to measuredistance between cluster key values of the record group. In some cases,a clustering algorithm and/or an unsupervised machine learning model canbe utilized to form record groups 1-X.

FIGS. 27A-27H illustrate embodiments of a record processing and storagesystem 2505 that communicates row confirmation data with one or moredata sources 2501 based on confirming receipt of, generating pages from,and/or storing records 2422 received from these data sources 2501. Overtime, data sources 2501 can resend certain records 2422 as necessarybased on row confirmation data indicating these records were notsuccessfully received and/or stored, for example, due to failures intheir transmission, failures in their storage, or failures intransmission of the corresponding row confirmation data. Due thisretransmission of certain records 2422 by data sources, the recordprocessing and storage system 2505 can further perform pagededuplication as pages are generated over time to ensure that duplicatedrows are removed from pages 2515 and/or will not be read from more thanone page 2515.

This mechanism of both confirming that all records 2422 are successfullystored in pages and also deduplicating any records that wereretransmitted over time improves database systems by ensuring that allrequired records 2422 will be read exactly once from pages 2515. Inparticular, this “exactly once” guarantee of record reads ensures thatqueries performed on records 2422 stored by the database system 10 areguaranteed to be correct, where each required record is included inprocessing queries, but is only read one time in processing queries.Furthermore, by shifting the responsibility of deduplicating rows to therecord processing and storage system 2505, data sources can beconservative in their transmission of rows by sending and possiblyresending rows. This improves for example, starting from a trackedtransmission starting point indicator that is simple for data sources tomaintain. This also further improves database systems by simplifying theprocessing required to confirm transmittal of records by allowing datasources to send records multiple times, while still guaranteeing theserecords will be deduplicated in durable storage as pages and/or assegments.

Some or all of the features and/or functionality of embodiments of therecord processing and storage system 2505 discussed in conjunction withFIGS. 27A-27H can be utilized to implement the record processing andstorage system 2505 of FIG. 25A and/or to implement any otherembodiments of record processing and storage system 2505 discussedherein. Some or all of the features and/or functionality of embodimentsof a data source 2501 discussed in conjunction with FIGS. 27A-27H can beutilized to implement some or all of the data sources 2501-1-2501-L ofFIG. 25A and/or to implement any other embodiments of a data source 2501discussed herein.

FIG. 27A illustrates such an embodiment of communication between arecord processing and storage system 2505 and a particular data source2501-1. The data source 2501 can implement a row transmission module2706 to transmit records 2422 of a record stream to the recordprocessing and storage system 2505 over time. The row transmissionmodule 2706 can utilize a row labeling module 3008 to generate a streamof labeled row data 3010 for transmission from a record stream ofrecords 2422. Each labeled row data 3010 can be generated by data source2501 to include a data source identifier 3014, a row number 3012, and/orrow data 2910. Example embodiments of labeled row data 3010 arediscussed in further detail in conjunction with FIGS. 27B-27D.

The labeled row data 3010 can be generated in accordance with rowtransmittal requirement data. The row transmittal requirement dataindicates instructions and/or rules for generating the labeled row data3010 from a stream of records. For example, embodiments of the labeledrow data 3010 described herein can be generated by data sources 2501based on the row transmittal requirement data. In some cases, the rowtransmittal requirement data includes application data that isdownloaded and/or installed by the data source 2501. For example, therow transmittal requirement data can be stored in memory of the datasource 2501 and can include operational instructions. These operationalinstructions, when executed by at least one processor of the data source2501, can cause the data source 2501 to 2501 to execute some or all ofthe functionality of the row labeling module 3008 and/or to execute someor all other functionality of the row transmission module 2706.

As illustrated in FIG. 27A, the row transmittal requirement data can bereceived from the record processing and storage system 2505 via a rowtransmittal requirement communication module 3002. In such cases, therow transmittal requirement data can be transmitted to one or more datasources 2501 by the record processing and storage system 2505. Therecord processing and storage system 2505 can determine this rowtransmittal requirement data, for example, based on generating the rowtransmittal requirement data, based on receiving the row transmittalrequirement data, based on the row transmittal requirement data beingconfigured via user input, based on retrieving the row transmittalrequirement data from memory, and/or by otherwise determining the rowtransmittal requirement data. Alternatively, the row transmittalrequirement data is otherwise determined by some or all data sources2501, for example, where data sources 2501 determine the row transmittalrequirement data based on generating the row transmittal requirementdata, based on receiving the row transmittal requirement data, based onthe row transmittal requirement data being configured via user input,based on retrieving the row transmittal requirement data from memory,and/or by otherwise determining the row transmittal requirement data.

The row numbers 3012 generated over time by a data source 2501 can eachbe distinct from all other row numbers 3012 generated by this datasource 2501 to uniquely identify the corresponding row data 2910, thusenabling deduplication of row data 2910 with same row numbers 3012 fromthe same data source 2501. The row numbers 3012 generated over time canfurther maintain an ordering in accordance with an ordering scheme. Inparticular, the row transmission requirement data can dictate anordering scheme that indicates rules regarding generation of and/orordering of row numbers 3012 included in labeled row data 3010 generatedby a data source 2501. For example, the row numbers 3012 for eachcorresponding labeled row data 3010 can be generated by the data source2501 as a function of when the labeled row data 3010 is generated and/oras a function of the placement of the corresponding one or more rows inthe record stream, in accordance with the ordering scheme. As discussedin further detail herein, adherence to such a row number ordering thatis known to both the data source 2501 and the record processing andstorage system 2505 can enable the data source 2501 to determine whichrecords to retransmit to the record processing and storage system 2505,while allowing the record processing and storage system 2505 to leveragethe known ordering to more easily deduplicate records included in itspages 2515.

In the examples discussed herein, the row numbers 3012 are generated asstrictly increasing numeric values in each subsequently generatedlabeled row data 3010 from records in the record stream. In suchordering schemes, row number 3012 included in each labeled row data 3010can be generated in accordance with a monotonically increasing function,where newer labeled row data 3010 has row numbers that are strictlygreater than older labeled row data. In such ordering schemes, the rownumber 3012 is not necessarily required to increase in fixed intervals,where each row number 3012 can increase from a previous row number 3012by any amount. As a particular example, the row numbers 3012 can begenerated to be equal to, based on, and/or a function of the bit offsetof the corresponding records 2422 in the record stream, such the bitoffset of the first record or last record included in the row data 2910of the labeled row data. As another particular example, the row numbers3012 can be generated to be equal to, based on, and/or a function of atimestamp associated with the corresponding records in the record streamand/or associated with the generating of the corresponding labeled rowdata 3010.

In other embodiments, row numbers can be generated in accordance withanother ordering scheme, for example, where row numbers are generatedinstead strictly decrease over time. Alternatively, row numbers can begenerated as any data type in accordance with any other ordering schemethat is known to both the data source and to the record processing andstorage system 2505, for example, based on being indicated in the rowtransmittal requirement data sent by the record processing and storagesystem 2505. Row numbers can be numeric values, can be a data type thatcan be converted to and/or represented as numeric values, and/or can beany data type that can be compared to other values of the data type todetermine an ordering. Different data sources can generate and/orincrement their row numbers in the same or different fashion and/or inaccordance with a same or different function, while all adhering to thesame ordering scheme.

As used herein, first row data is older than second row data based onbeing generated before the second row data and/or based on its one ormore records 2422 being received and/or generated previous to the one ormore records 2422 in the record stream. In the examples discussedherein, a first row number is more favorably ordered than a second rownumber when the first row number is less than the second row number,based on the first row number corresponding to row data that istherefore older than the row data denoted by the second row number. Inother embodiments, where the row numbers are instead generated tostrictly decrease, a first row number is more favorably ordered than asecond row number when the first row number is greater than the secondrow number, based on the first row number corresponding to row data thatis therefore older than the row data denoted by the second row number.For any other types ordering and/or labeling scheme for row numbers 3012in other embodiments, a first row number is more favorably ordered thana second row number when the first row number is otherwise determined tocorrespond to row data that is older than the row data denoted by thesecond row number in accordance with the corresponding ordering.

As labeled row data 3010 is generated from rows of the correspondingrecord stream over time by the row labeling module 3008, the generatedlabeled row data 3010 is included in a confirmation-pending row list3020. The confirmation-pending row list 3020 can be implemented by atleast one memory such as cache memory of the data source 2501 to storethe labeled row data 3010 as it awaits transmission, confirmation, andpossibly retransmission one or more additional times. The data source2501 can send labeled row data 3010 included in the confirmation-pendingrow list 3020, for example, based on an ordering of the labeled row data3010 in the confirmation-pending row list 3020 in accordance with rownumbers 3012 and/or based on row list update data 3035 generated overtime. An example embodiment of sending labeled row data 3010 from theconfirmation-pending row list 3020 over time is discussed in furtherdetail in conjunction with FIGS. 27E-27H.

In response to labeled row data 3010 received over time, the recordprocessing and storage system 2505 can implement page generator 2511 asdiscussed previously to generate new pages 2515 for storage in pagestorage system 2506, for example, to await conversion into segmentsand/or to service queries as discussed previously. The page generator2511 can further implement a row deduplication module 3050 to removeduplicated records from pages and/or to otherwise ensure that anyrecords received in multiple labeled row data 3010 over time are readexactly once in reads to pages 2515, even if these records are stored inmultiple pages 2515 generated by page generator 2511. Exampleembodiments of the row deduplication module 3050 of FIG. 27A arediscussed in further detail in conjunction with FIGS. 28A-28D, FIGS.29A-29B, and FIG. 30A.

As various labeled row data 3010 are received over time and/or as pages2515 are generated over time, the record processing and storage system2505 can generate row confirmation data 3030. The row confirmation data3030 can indicate row data 2910 that is confirmed by the recordprocessing and storage system 2505, where this row data 2910 that isconfirmed will not need to be retransmitted by the corresponding datasource 2501. The data source 2501 can receive various row confirmationdata 3030 over time, and can utilize a confirmation update module 3040to generate row list update data 3035 as each new row confirmation datais received. Each new row list update data 3035 can be applied to theconfirmation-pending row list 3020 over time to update labeled row dataincluded in the confirmation-pending row list and/or to update atransmission starting point indicator of the confirmation-pending rowlist.

In some cases, confirmed row data corresponds to row data that issuccessfully received. The record processing and storage system 2505 cangenerate row confirmation data indicating that one or more particularrow data 2910 is successfully received based receiving this particularrow data 2910 in a particular labeled row data 3010.

Alternatively or in addition, confirmed row data corresponds to row data2910 that is successfully included in a page 2515 generated by the pagegenerator 2511. The record processing and storage system 2505 cangenerate row confirmation data indicating that one or more particularrow data 2910 is successfully included in a page 2515 based ongenerating one or more pages 2515 to include this particular row data2910, based on deduplicating the one or more pages into a deduplicatedpage as discussed in further detail in conjunction with FIGS. 28A-28D,and/or based on storing these one or more pages 2515 in page storagesystem 2506. Ensuring the row data 2910 was successfully converted intoa page before indicating this row data 2910 in row confirmation data canbe ideal to account for failure that may occur after the row data 2910is received and before the row data 2910 is included in a page 2515.

Alternatively or in addition, confirmed row data corresponds to row data2910 that is durably stored in page storage system 2506. As used herein,a record 2422 can be considered “durably stored” if at least a thresholdlevel of fault tolerance is attained in storing of the record. Forexample, after the row data 2910 is durably stored and thus is stored inaccordance with the threshold level of fault tolerance, failure thatwould render the row data 2910 irrecoverable is not expected to occur.Because row data 2910 is only considered to be immune from expectedlevels of failure once it is durably stored, ensuring the row data 2910is durably stored before indicating this row data 2910 in rowconfirmation data can be ideal to account for failure that may occurprior to durable storage of the row data 2910. In such cases, theprocessing and storage system 2505 can generate row confirmation dataindicating that one or more particular row data 2910 is durably storedin the page storage system 2506 based on successfully storing one ormore pages 2515 that include the particular row data 2910 durably inpage storage system 2506.

In some cases, durable storage of a record requires more fault-tolerantmeans of storage than being stored in page cache 2512 after beinggenerated by a page generator 2511. For example, replicating a givenpage 2515 into a set of replicas and storing the set of replicas indifferent locations to enable recovery of the given page 2515 for up toa threshold number of storage failures can render records 2422 in thegiven page 2515 as durably stored. As another example, records includedin a page 2515 are considered durably stored when the page 2515 issuccessfully stored in page storage 2546 of a long term storage 2540. Asanother example, records included in a page 2515 are considered durablystored a threshold number of replicas of the page 2515 are successfullystored in page storage 2546 of a corresponding number of different longterm storage 2540. As another example, generating a segment group 2522from a set of records 2422 in a record group 2625 in accordance with aredundancy storage coding scheme and storing each segment 2424 of thesegment group 2522 in different locations renders this set of records2422 as durably stored.

Each row confirmation data 3030 can indicate one or more row data thatis confirmed. In particular, the row confirmation data 3030 can begenerated by the record processing and storage system 2505 to include orotherwise indicate one or more row numbers 3012 that correspond to rowdata 2910 that is designated as confirmed row data. Each rowconfirmation data 3030 generated by the record processing and storagesystem 2505 for a data source 2501 over time can indicate row numbers3012 of any new row data that has been received since most previouslygenerated and transmitted row confirmation data 3030 for the datasource.

For example, the record processing and storage system 2505 generates therow confirmation data 3030 to indicate the row numbers 3012 included inlabeled row data 3010 that include row data 2910 that is confirmed bythe record processing and storage system 2505. In particular, the rowconfirmation data 3030 for a given data source 2501 can include and/orotherwise indicate all row numbers 3012 for all row data 2910 that wasconfirmed since the last generation and transmission of row confirmationdata 3030 for the given data source 2501.

As another example, the record processing and storage system 2505alternatively or additionally generates the row confirmation data 3030to indicate a span of row numbers 3012, such as only maximum and/orminimum row number, with corresponding row data 2910 that is confirmedby the record processing and storage system 2505. In particular, the rowconfirmation data 3030 can indicate a span of row numbers 3012 based onrow data 2910 that was most-recently confirmed and/or that was confirmedsince the last generation and transmission of row confirmation data3030. In some cases, the row confirmation data 3030 can further includea number of different row data 2910 that are included in this span ofrow numbers to further indicate the number of different row data 2910that is confirmed, enabling the data source 2501 to determine whether ornot all row data 2910 with row numbers 3012 in the corresponding span ofrow numbers 3012 were confirmed.

As another example, the record processing and storage system 2505alternatively or additionally generates the row confirmation data 3030to include a horizon row number, where all row data 2910 with rownumbers 3012 that are more favorably ordered than the horizon row numberin an ordering of the corresponding row numbers 3012 are guaranteed tobe confirmed. A particular example embodiment of this horizon row numberis implemented as a durability value, where all row data 2910 with rownumbers 3012 that are more favorably ordered than the durability valuein an ordering of the corresponding row numbers 3012 are guaranteed tobe durably stored. Embodiments of a record processing and storage system2505 that determined and communicates this durability value arediscussed in further detail in conjunction with FIGS. 32A-32B.

Each row confirmation data 3030 can be generated by the page generator2511 as illustrated in FIG. 27A. For example, the page generator 2511generates the source's row confirmation data 3030 based on the labeledrow data 3010 that it receives, that is generates pages from, that itfacilitates durable storage of, and/or otherwise confirms.Alternatively, other processing resources of the record processing andstorage system 2505 can be utilized to generate some or all rowconfirmation data 3030 the based on the labeled row data 3010 that itreceives, generates pages from, durably stores, and/or otherwiseconfirms.

Each row confirmation data 3030 can be generated for transmission backto the corresponding data source 2501 based on: a predefined schedule ofgenerating the row confirmation data 3030; predefined time intervals forgenerating the row confirmation data 3030; receiving an instruction togenerate the row confirmation data 3030; determining a threshold amountof time has passed since generating the most recent row confirmationdata 3030 for the data source 2501; determining a new row data with thedata source's data source identifier 3014 has been confirmed, where eachrow confirmation data 3030 indicates one row number for onecorresponding row data; determining at least a threshold number of newrow data with the data source's data source identifier 3014 has beenconfirmed; and/or based on another determination to generate each rowconfirmation data 3030 over time.

A confirmation communication module 3004 of the record processing andstorage system 2505 can be implemented via at least one transmitterand/or communication interface of the record processing and storagesystem 2505. The confirmation communication module 3004 can send eachrow confirmation data 3030 to the corresponding data source 2501 as itis generated by the record processing and storage system 2505.

FIGS. 27B-27D illustrate example embodiments of the labeled row data3010. Each labeled row data 3010 can be generated to include a datasource identifier 3014 corresponding to the data source, for example,where each data source 2501-1-2501-L has a different data sourceidentifier 3014 to differentiate records received from different datasources. Each labeled row data 3010 can be generated to alternatively oradditionally include row data 2910, which can be implemented as the rowdata 2910 of FIG. 25C. The row data 2910 can otherwise a single record2422 or a batch of multiple records 2422. Each labeled row data 3010 canbe generated to alternatively or additionally include at least one rownumber 3012 corresponding to row data 2910.

In some cases, as illustrated in FIG. 27B, the labeled row data 3010 hasrow data 2910 that includes exactly one record 2422. The row number 3012thus corresponds to the particular record 2422. When all labeled rowdata 3010 is generated in this fashion, each row number corresponds toexactly one particular record 2422.

In some cases, as illustrated in FIG. 27C, the labeled row data 3010 hasrow data 2910 that includes a set of multiple records 2422 as a batch ofrecords. The row number 3012 can thus corresponds to the batch ofrecords in row data 2910 as a whole, where individual records 2422optionally do not have their own row numbers 3012. In such cases, when arow number 3012 is indicated in row confirmation data 3030, the entireset of multiple records 2422 in the corresponding has row data 2910 isindicated as confirmed. In such embodiments, batches of records 2422 canbe processed in tandem by the record processing and storage system toensure that the records 2422 remain together in a batch. For example,all of the set of multiple records 2422 in given row data 2910 areincluded in a same page 2515. This can ensure that, when confirming abatch of records with the corresponding row number 3012, all of the setof multiple records are guaranteed to be confirmed based on beingprocessed together. As used herein, a row number 3012 that correspondsto a set of multiple records 2422 in labeled row data 3010 in thisfashion can be interchangeably referred to as a “batch number.”

In some cases, as illustrated in FIG. 27D, the labeled row data 3010 hasrow data 2910 that includes a set of multiple records 2422 as a batch ofrecords with a corresponding batch number 3412 denoting the batch ofrecords as a whole, and that also preserves individual row numbers 3012for each record 2422. For example, the batch of records included rowdata 2910 as illustrated in FIG. 27C is utilized to implement the rowdata 2910 of FIG. 27D, where each record 2422 further is assigned itsown row number 3012 as illustrated in FIG. 27B. The batch number 3412 ofFIG. 27D can therefore be implemented utilizing the row number 3012 ofFIG. 27C. In some cases, a separate batch number 3412 is not generatedand/or included, where the batch number 3412 for the batch of rows inrow data 2910 is denoted as and/or is automatically set as the lowestrow number 3012 and/or the highest row number 3012 included in thecorresponding batch of rows. Distinguishing each row with their own rownumbers 3012 can be useful in confirming individual rows 3012, forexample, in cases where portions of a batch are not able to beconfirmed. Further distinguishing each batch of rows with their ownbatch numbers 3412 can be useful in confirming and/or otherwiseidentifying the batch as a whole. In such embodiments, row confirmationdata 3030 can be generated to indicate particular row numbersindividually to denote individual records of the row confirmation data3030 are confirmed, and/or can indicate the batch number to denote theentire set of multiple records 2422 in the corresponding has row data2910 is confirmed. In some cases, some or all embodiments of row number3012 discussed herein can be implemented utilizing the batch number 3412of FIG. 27D, for example, in embodiments where the row number 3012corresponds to a set of multiple rows, even if the labeled row data 3010optionally includes the individual row numbers 3012 for each of the setof multiple rows as illustrated in FIG. 27D.

FIG. 27E illustrates an embodiment where multiple data sources 2501communicate with the record processing and storage system 2505 asdiscussed in conjunction with FIG. 27A. In embodiments with multipledata sources 2501-1-2501-L, each labeled row data 3010 generated andtransmitted by a given data source 2501 indicates a same data sourceidentifier 3014. For example, all labeled row data 3010 sent by datasource 2501-1 indicates a first data source identifier 3014-1, alllabeled row data 3010 sent by data source 2501-2 indicates a secondsource identifier 3014-2, and so on.

Different corresponding row confirmation data 3030 can be generated andtransmitted to each data sources 2501-1-2501-L over time. For example,the data source identifier 3014 of confirmed row data can indicate whichparticular data source's row confirmation data 3030 will indicatecorresponding row numbers 3012. Each row confirmation data 3030 thusindicates only row numbers for a corresponding one of a plurality ofdata sources to which the row confirmation data 3030 is transmitted.

Furthermore, each data source can independently generate its own rownumbers to generate its labeled row data 3010, for example, inaccordance with the row transmittal requirement data. Because labeledrow data 3010 includes data source identifiers 3014, identical rownumbers received from different data sources 2501 will not be confusedand the ordering of row numbers received from each data sources 2501 canbe maintained. This enables data sources to generate row numbers withoutcoordination, while ensuring that records can be deduplicated by therecord processing and storage system, for example, as discussed infurther detail in conjunction with FIGS. 28A-28D. Each data source 2501can further adhere to the same row number ordering scheme, for example,where all data sources 2501 generate their own row numbers over timethat strictly increase in value.

In some cases, a same computing device and/or corresponding transmittercan implement multiple data sources 2501. For example, each data source2501 corresponds to a different table and/or different types of recordsin corresponding different record streams of a same computing deviceand/or a same entity. A same transmitter and/or communication interfacecan receive and/or generate these multiple record streams, and cangenerate and send labeled row data for each of its record streams to therecord processing and storage system 2505. In such cases, this samecomputing device can assign different source IDs to different labeledrow data based on including records 2422 from different ones of itsrecord streams to differentiate the different record streams.

FIGS. 27F-27H illustrate an example of a row transmission module 2706 ofa data source 2501 that maintains its confirmation-pending row list 3020to send labeled row data 3010 over time. The confirmation-pending rowlist 3020 is updated over time based on the row confirmation data 3030received over time from the record processing and storage system 2505.

A data source 2501 can maintain its confirmation-pending row list 3020as a sorted list of labeled row data 3010 by row number 3012. Forexample, the confirmation-pending row list 3020 can be implemented asand/or based on a queue and/or priority queue that is populated withlabeled row data 3010 as it its generated. The ordering of the labeledrow data 3010 is in accordance with the ordering scheme utilized togenerate the row numbers 3012. In this example, row numbers aregenerated with an ordering scheme to strictly increase over time, andthus labeled row data is sorted by row number 3012 where lower rownumbers 3012 are ordered before higher row numbers 3012 based on thelabeled row data 3010 with the lower row numbers 3012 having beengenerated prior to labeled row data 3010 with the higher row numbers3012. At a given time, the confirmation-pending row list 3020 mayinclude some labeled row data 3010 that has already been transmitted atleast once, and/or may include other labeled row data 3010 that has notbeen transmitted yet.

The labeled row data 3010 is transmitted in an ordered stream over timebased on their corresponding ordering in the confirmation-pending rowlist 3020, where the labeled row data 3010 with the most favorablyordered row data is sent first. The data source 2501 can continue tosend labeled row data 3010 in accordance with a corresponding orderingin the confirmation-pending row list 3020, for example, until apredetermined number of labeled row data 3010 are transmitted and/oruntil row confirmation data 3030 is received to cause theconfirmation-pending row list 3020 to be updated.

When row confirmation data 3030 is received, the confirmation updatemodule 3040 can update the confirmation-pending row list 3020 to updatea tracked transmission starting point indicator 3025 to indicate labeledrow data 3010 in the confirmation-pending row list 3020 to become thefirst ordered labeled row data 3010 in the confirmation-pending row list3020 for resuming retransmission of the labeled row data 3010 in theconfirmation-pending row list 3020. This identified starting labeled rowdata 3010 is selected based on all other labeled row data prior to thislabeled row data 3010 having been confirmed in row confirmation data3030. For example, this identified starting labeled row data is selectedto be the least favorably ordered labeled row data 3010 that meets thiscondition. All labeled row data 3010 with more favorably ordered rownumbers than the updated tracked transmission starting point indicator3025 can be removed from and/or ignored in the confirmation-pending rowlist 3020 based on being indicated as confirmed, and are notretransmitted.

In some embodiments, as illustrated in the example of FIGS. 27F-27H,only the tracked transmission starting point indicator 3025 is changedin updates to the confirmation-pending row list 3020. In such cases, oneor more labeled row data 3010 after the tracked transmission startingpoint indicator 3025 may have been confirmed in row confirmation data3030 and/or may otherwise already be received, stored, and/or durablystored, but it still retransmitted based on being after the trackedtransmission starting point indicator 3025 in the confirmation-pendingrow list 3020. This can be ideal, as the update simply involves shiftingthe position of the tracked transmission starting point indicator 3025,and can be easier to maintain by the data source as it queues largenumbers of labeled row data 3010 for transmission at high transmissionrates. This also leverages the deduplication responsibilities of therecord processing and storage system by conservatively retransmittingrecords. In some cases, this can be further ideal by reducing the amountof information required in row confirmation data 3030. For example, therow confirmation data 3030 can be generated by the record processing andstorage system 2505 in some cases to depict conservative confirmationinformation, and not necessarily indicate all confirmed rows.

FIG. 27F illustrates a confirmation-pending row list 3020 at a firsttime t₁. At this time, the confirmation-pending row list 3020 includes aset of labeled row data 3010-100, 3010-105, 3010-200, and 3010-220.These labels depicted in FIG. 27E are based on corresponding numbers oflabeled row data 3010 in this example being equal to 100, 105, 200, and220. Based on the tracked transmission starting point indicator 3025indicating labeled row data 3010-100, the labeled row data 3010 istransmitted by row transmission module 2706, starting with labeled rowdata 3010-100 in accordance with the ordering scheme by row number, assorted in the confirmation-pending row list 3020.

FIG. 27G illustrates this confirmation-pending row list 3020 at a secondtime t₂ after transmission of labeled row data 3010-100, 3010-105,3010-200, and 3010-220. At this time, row confirmation data 3030 isreceived indicating row numbers 100, 105, and 220. Because row number200 was not indicated in the row confirmation data 3030, labeled rowdata 3010-200 is identified as the new starting point by confirmationupdate module 3040 based on all previous labeled row data 3010 havingbeen confirmed. This is reflected in the update to tracked transmissionstarting point indicator 3025 to indicate labeled row data 3010-200. Inthis case, labeled row data 3010-220 will be retransmitted despitehaving been confirmed based on more favorably ordered labeled row data3010-200 requiring retransmission. In other embodiments, labeled rowdata 3010-220 is removed from the confirmation-pending row list 3020based on having been confirmed and is not retransmitted.

FIG. 27H illustrates this confirmation-pending row list 3020 at a thirdtime t₃ after the tracked transmission starting point indicator 3025 isupdated. Based on the tracked transmission starting point indicator 3025indicating labeled row data 3010-200, the row transmission module 2706sends labeled row data, starting with labeled row data 3010-200, inaccordance with the ordering. Note that labeled row data 3010-200 andlabeled row data 3010-200 are retransmitted, while new labeled row dataincluding row data 3010-230 and labeled row data 3010-250 aretransmitted for the first time. This process of transmitting labeled rowdata 3010 over time based on the ordering of labeled row data 3010 inthe confirmation-pending row list 3020 and further based on updates tothe tracked transmission starting point indicator 3025 of theconfirmation-pending row list 3020 over time can be continued over time.

While FIGS. 27F-27H illustrate the case where updates toconfirmation-pending row list 3020 are achieved via a simple shift of atracked transmission starting point indicator 3025, other embodiments ofconfirmation update module 3040 can involve other updates to theconfirmation-pending row list 3020. In some cases, all labeled row data3010 indicated in row confirmation data 3030 is removed from theconfirmation-pending row list 3020, regardless of its ordering inconfirmation-pending row list 3020. For example, labeled row data3010-220 is removed from the confirmation-pending row list 3020 inupdating the confirmation-pending row list 3020 based on having beenconfirmed in the row confirmation data 3030. This can be ideal tominimize the number of retransmissions required by the row transmissionmodule 2706 to more quickly populate the database system 10 with newdata rather than retransmitting redundant data that will bededuplicated.

In some cases where the confirmation-pending row list 3020 is updated inthis fashion, the only labeled row data 3010 that need be deduplicatedby the record processing and storage system 2505 corresponds to labeledrow data 3010 with row numbers that were confirmed, but whose rowconfirmation data 3030 encountered some delay, some transmissionfailure, and/or was otherwise not communicated and/or processed by thecorresponding data source 2501. This causes the data source to re-sendthis labeled row data 3010 that was actually confirmed because the datasource was never made aware that this labeled row data 3010 wasconfirmed. This retransmitted labeled row data 3010 can be deduplicatedby the record processing and storage system 2505.

FIG. 27I illustrates a method for execution by a record processing andstorage system 2505. For example, the database system 10 can utilize atleast one processing module of one or more nodes 37 of one or morecomputing devices 18, where the one or more nodes execute operationalinstructions stored in memory accessible by the one or more nodes, andwhere the execution of the operational instructions causes the one ormore nodes 37 to execute, independently or in conjunction, the steps ofFIG. 27I. Some or all of the method of FIG. 27I can be performed by thepage generator 2511 and/or the page storage system 2506 of FIG. 25A.Some or all of the method of FIG. 27I can be performed by one or morestream loader modules 2510 of FIG. 25B, independently or in conjunction.Some or all of the method of FIG. 27I can be performed by the rowtransmittal requirement communication module 3002 of FIG. 27A, the pagegenerator 2511 of FIG. 27A, the row deduplication module 3050 of FIG.27A, and/or the confirmation communication module 3004 of FIG. 27A. Someor all of the steps of FIG. 27I can optionally be performed by any otherprocessing module of the database system 10. Some or all of the steps ofFIG. 27I can optionally be performed by one or more data sources 2501,for example, by utilizing the row transmission module 2706, and/or canbe performed via communication with one or more data sources 2501. Forexample, a record processing and storage system 2505 performs the stepsof FIG. 27I in conjunction with communication with a data source thatperforms the steps of 27J. Some or all of the steps of FIG. 27I can beperformed to implement some or all of the functionality of the recordprocessing and storage system 2505 of FIG. 25A and/or FIG. 25B. Some orall of the steps of FIG. 27I can be performed to implement some or allof the functionality of the row deduplication module 3050 of FIG. 28B,FIG. 28C, and/or FIG. 28D. Some or all steps of FIG. 27I can beperformed by database system 10 in accordance with other embodiments ofthe database system 10 and/or nodes 37 discussed herein.

Step 2772 includes receiving a plurality of labeled row data from eachof a plurality of data sources. The plurality of labeled row data can bein accordance with row transmittal requirement data, for example, wherethe method further includes sending the row transmittal requirement datato the plurality of data sources. Each labeled row data received from agiven data source can include at least one record; a corresponding rownumber; and a data source identifier corresponding to the given datasource. The data source can generate labeled row data over time, whereeach labeled row data has a corresponding row number that is strictlygreater than all previous row numbers if row numbers are generated asmonotonically increasing values, or that is strictly less than allprevious row numbers if row numbers are generated as monotonicallydecreasing values.

Step 2774 includes generating a plurality of pages from records includedin each plurality of labeled row data received from each of theplurality of data sources. Step 2776 includes storing the plurality ofpages in a page storage system. Information regarding row numbers and/ordata sources of the records included in the plurality pages can beincluded in page metadata stored in the plurality of pages and/or mappedto the plurality of pages. The plurality of pages can be stored inmemory, such as via page storage system 2506.

Step 2778 includes generating a plurality of row confirmation data foreach of the plurality of data sources based on the plurality of labeledrow data. Step 2780 includes transmitting each plurality of rowconfirmation data to the each of the plurality of data sources. Theplurality of row confirmation data confirming a set of records beingwere received can be generated and transmitted before and/or after thegenerating and storing the plurality of pages from this set of records.For example, the row confirmation data sent to a data source indicates:one or more row numbers of one or more labeled row data successfullyreceived from the data source in step 2774; one or more row numbers ofone or more labeled row data whose records are successfully included inpages generated in step 2776; and/or one or more row numbers of one ormore labeled row data whose records are durably stored by the recordprocessing and storage system.

The row confirmation data can indicate row numbers from thecorresponding data source that were received and/or stored, for example,within a time frame. For example, the time frame is a fixed intervaland/or corresponds to a period of time since a most recent rowconfirmation data of a plurality of previous row confirmation data wassent to the data source. The row confirmation data can indicate each rownumber whose records were received and/or stored within the time frame.The row confirmation data can alternatively or additionally indicate amaximum and/or minimum row number of a set of multiple records that werereceived and/or stored within the time frame. The row confirmation datacan alternatively or additionally indicate a number or records receivedand/or stored within the time frame.

Step 2782 includes facilitating deduplication of duplicated rowsincluded the plurality of pages based on the labeled row data. Forexample, the rows are deduplicated based on removing records from pagesthat were included in labeled row data with row numbers and same datasource identifiers, indicating these records were duplicated. Forexample, the method can include receiving sets of labeled row datahaving the same row numbers and the same data identifier, indicating thecorresponding records are duplicates sent by a data source in multipletransmissions. The data source can send labeled row data for a givenrecord with the same row numbers in multiple transmissions based onreceiving row confirmation data indicating that this given record wasnot received in one or more previous transmissions of the given record,for example, based on the row confirmation data received by the datasource not indicating the row number for the given record for a timeframe that the given record was transmitted by the data source.

In various embodiments, the method can further include performing queryexecutions on records in pages that have been deduplicated in step 2782,for example, to guarantee query correctness by guaranteeing rows areincluded exactly once based on the deduplication. The method can furtherinclude performing a conversion process on pages that have beendeduplicated in step 2782 to generate a plurality of segments forlong-term storage, where no record is included in more than one segmentbased on the deduplication.

FIG. 27J illustrates a method for execution by a data source 2501. Forexample, the data source 2501 can include at least one processor and atleast one memory, where the at least one memory stores operationalinstructions that, when executed by the at least one processor, causethe data source 2501 to execute some or all of the steps of FIG. 27J.Some of all of the method of FIG. 27J can be performed in accordancewith execution of application data stored by the data source 2501, wherethe application data is received from and/or associated with the recordprocessing and storage system 2505. Some or all of the method of FIG.27J can be performed by one or more data sources 2501 of FIG. 25A. Someor all of the method of FIG. 27J can be performed by the rowtransmission module 2706 of FIG. 27A, for example, by implementing therow labeling module 3008, the confirmation-pending row list 3020, and/orthe confirmation update module 3040. Some or all of the steps of FIG.27J can optionally be performed by any other processing module of thedatabase system 10. Some or all of the steps of FIG. 27J can optionallybe performed by the record processing and storage system 2505, and/orcan be performed via communication with the record processing andstorage system 2505. For example, a data source 2501 performs the stepsof FIG. 27J in conjunction with communication with a record processingand storage system 2505 that performs the steps of 27I. Some or all ofthe steps of FIG. 27J can be performed to implement some or all of thefunctionality of one or more data sources 2501 of FIG. 25A and/or FIG.27A. Some or all steps of FIG. 27J can be performed by database system10 in accordance with other embodiments of the database system 10 and/ornodes 37 discussed herein.

Step 2784 includes generating a plurality of labeled row data fromrecords in a record stream. Each record row data can be generated inaccordance with row transmittal requirement data, for example, where themethod further includes receiving the row transmittal requirement datafrom a record processing and storage system and/or storing the rowtransmittal requirement data in memory. Each labeled row data caninclude at least one record of the record stream; a corresponding rownumber; and a data source identifier corresponding to the given datasource. Row numbers can be generated for records as they are processedfrom the record stream, where the values of row numbers have an orderingin accordance with the ordering that they are generated forcorresponding records. For example, row numbers can be numeric valuesgenerated to be monotonically increasing values and/or monotonicallydecreasing values in accordance with the ordering they are generatedand/or the ordering of the corresponding records in the record stream.All row numbers generated by the source are different from all other rownumbers generated by the source. In some cases, row numbers aregenerated in accordance with irregular intervals from record to record.For example, the row numbers can be generated to be equal to and/or as afunction of a bit offset of corresponding records in the record stream,where different records are different data lengths based on having atleast one variable-length field.

Step 2786 includes each of the plurality of labeled rows in aconfirmation-pending row list. For example, the confirmation-pending rowlist is an ordered list of labeled row data by row number of the labeledrow data. Step 2788 includes transmitting a first sequentially orderedset of the plurality of labeled row data in the confirmation-pending rowlist to a record processing and storage system. A first ordered labeledrow data of the first sequentially ordered set can be determined basedon a tracked transmission starting point indicator maintained by thedata source. The tracked transmission starting point indicator canindicate a lowest valued row number and/or first ordered row number, forexample, in the case where row numbers monotonically increasing numericvalues, that has not yet been confirmed in previously received rowconfirmation data by the record processing and storage system. A numberof labeled row data for transmission and/or total data size of labeledrow data for transmission can optionally be predetermined and/orreceived in the row transmittal requirement data. In some cases, labeledrow data is sent in sequence, one at a time, where the firstsequentially ordered set of the plurality of labeled row data includesall labeled row data sent prior to receiving row confirmation data instep 2790.

Step 2790 includes receiving row confirmation data from the recordprocessing and storage system. The row confirmation data can indicaterow numbers for records in the first sequentially ordered set that havebeen confirmed by the record processing and storage system to have beenreceived and/or stored. For example, in response to the firstsequentially ordered set of the plurality of labeled row data beingtransmitted by the data source in performing step 2788, the recordprocessing and storage system receives some or all of the firstsequentially ordered set of the plurality of labeled row data inperforming step 2772, generates pages from some or all of the firstsequentially ordered set of the plurality of labeled row data inperforming step 2774, and/or stores the generated pages that includesome or all of the first sequentially ordered set of the plurality oflabeled row data in performing step 2776. In performing step 2778, therecord processing and storage can generate row confirmation data for thedata sources based on the ones of the first sequentially ordered set ofthe plurality of labeled row successfully received, processed and/orstored in steps 2772, 2774 and/or 2776. The data source can receive theconfirmation data from the record processing and storage system in step2790 based on the record processing and storage system transmitting thisrow confirmation data to the data source in performing step 2778.

Step 2792 includes generating an updated confirmation-pending row listbased on the row confirmation data. For example, the trackedtransmission starting point indicator can be updated based on the rowconfirmation data to reflect the new lowest valued row number that hasnot yet been confirmed in this row confirmation data or in previous rowconfirmation data. As another example, the updated confirmation-pendingrow list can be generated to remove some or all labeled row data whoserow numbers are indicated in the row confirmation data. Step 2794includes transmitting a second sequentially ordered set of the pluralityof labeled row data in the updated confirmation-pending row list to therecord processing and storage system. For example, at least one labeledrow data in the first sequentially ordered set is included in the secondsequentially ordered set based on having a corresponding row number notindicated in the row confirmation data. Steps 2788, 2790, 2792, and/or2794 can be repeated over time each subsequent sequentially ordered setof the plurality of labeled row data.

FIGS. 28A-28D illustrate embodiments of a record processing and storagesystem 2505 that deduplicates pages. Some or all features and/orfunctionality of embodiments of record processing and storage system2505 of FIGS. 28A-28D can be utilized to implement the record processingand storage system 2505 of FIG. 25A and/or any other embodiments of therecord processing and storage system 2505 discussed herein. Some or allfeatures and/or functionality of embodiments of row deduplication module3050 of FIGS. 28A-28D can be utilized to implement the row deduplicationmodule 3050 of FIG. 27A.

As discussed previously, a record processing and storage system 2505 cangenerate and send row confirmation data 3030 to data sources based onreceiving and/or storing corresponding row data 2910. However, there isno guarantee that row confirmation data 3030 will be transmitted andreceived by a corresponding data source 2501 without failure. The datasource 2501 will therefore retransmit labeled row data 3010 that wasalready received and stored by the record processing and storage system2505 if its corresponding row confirmation data 3030 encountered atransmission error. To guarantee that records are read exactly once inquery executions and/or to guarantee that records are included exactlyonce in segments generated from pages 2515, this retransmitted row data2910 must be deduplicated. Furthermore, in cases where data sources 2501more conservatively retransmits labeled row data, for example, asdiscussed in conjunction within FIGS. 27F-27H, additional row data mustbe deduplicated. The page deduplication by the record processing andstorage system of FIGS. 28A-28D improves the technology of databasesystems by guaranteeing records are read exactly once, even though it ispossible for these records to be sent, stored, and processed more thanonce.

Additionally, because row data 2910 may be received and/or processed athigh data rates, it is not feasible to iterate over rows individuallylooking for duplicate records. To reduce the processing required toidentify and remove duplicate records, the ordering scheme utilized togenerate row numbers 3012 in labeled row data 3010 discussed inconjunction with FIGS. 27A-27H can be leveraged to perform pagededuplication based on simple comparisons of and updates to pagemetadata. In particular, because all row numbers 3012 generated by eachgiven source are guaranteed to be in accordance with a same orderingscheme, the record processing and storage system 2505 can be operable togenerate page metadata to include row number span data based on the rownumbers 3012 included in a page, and to deduplicate pages based oncomparing and/or updating row number span data included in pagemetadata. This improves database systems by enabling high data rates tobe received and processed by reducing the processing required to performdeduplication.

Finally, as discussed previously, it is ideal to minimize data movementof records stored in pages while awaiting conversion into segments, forexample, due to these high data rates of receiving and/or processingincoming data. The record processing and storage system 2505 can furtherbe operable to deduplicate records 2422 included in pages by “ignoring”spans of row numbers that overlap with other pages during page reads.For example, records with row numbers indicated to be ignored areskipped over and/or otherwise not returned in reads to the correspondingpages. This can be achieved, for example, based on updates to the pagemetadata, where the corresponding records are not deleted from and/ormoved from the page to avoid the any shifting of records within the pageand/or to otherwise reduce data movement. This is ideal in ensuring thatrecords are returned exactly once in reads to pages for query executionsand/or for generation of segments. This improves database systems byguaranteeing the “exactly once” reads of records stored in pages viadeduplication, while minimizing data movement to improve the efficiencyof the database system as discussed previously. As used herein,“removing” duplicated records from pages can correspond to updating thepage metadata to ensure that these “removed” records are not returned inreads to pages, even if they are maintained in storage of the page toreduce data movement. For example, page metadata is modified to removeduplicated records, while the pages themselves are not modified toremove these duplicated records.

As illustrated in FIG. 28A, a page generator 2511 can generate pages2515 from labeled row data 3010 that include one or more row data 2910as discussed previously. The page generator 2511 can further the utilizeinformation extracted from the corresponding labeled row data 3010 togenerate page metadata 3115 for each corresponding page 2515. The pagemetadata 3115 can be included in the corresponding page 2515, can bestored in conjunction with the corresponding page 2515, and/or canotherwise be mapped to the corresponding page 2515 in page storagesystem 2506.

The page metadata 3115 can be generated to include row number span data3119 for each data source whose row data 2910 is included in thecorresponding page, as denoted by data source identifiers 3014 of thecorresponding labeled row data 3010. Each row number span data 3119 canindicate a span of row numbers 3012, for example, in accordance with theordering scheme. For example, the row number span data 3119 for eachdata source 2501 can simply include only a minimum row number 3117 and amaximum row number 3118.

For a given page, the minimum row number 3117 and the maximum row number3118, per data source identifier 3014, of all rows present in the pagecan be determined by leveraging the information included in one of morelabeled row data 3010 of the given page. When the page is generated, theminimum row number 3117 is set as the lowest valued row number 3012and/or the most favorably ordered row number 3012 included in the page2515 for the corresponding data source identifier 3014. When the page isgenerated, the maximum row number 3118 is set as the highest valued rownumber 3012 and/or the least favorably ordered row number 3012 includedin the page 2515 for the corresponding data source identifier 3014.

As depicted in FIG. 28A, page metadata 3115 for a given page can have upto L row number span data 3119-1-3119-L based on the page generatorprocessing labeled row data from L different data sources 2501-1-2501-Lwith L different corresponding data source identifiers 3014-1-3014-L.Note that row number span data need not be generated or included fordata sources with no row data 2910 in the corresponding page. Note thatpages with row data 2910 from only one data source only have one rownumber span data 3119.

FIG. 28B illustrates utilizing a row deduplication module 3050 to updatea given page 2515 from its original state as a pre-deduplication page3145 to an updated state as a deduplicated page 3155. The rowdeduplication module 3050 of FIG. 28B can be utilized to implement therow deduplication module 3050 of FIG. 28A and/or any other embodimentsof the row deduplication module 3050 described herein.

A deduplicated page 3155, once generated, can be stored in the pagestorage system 2506 and/or can otherwise be denoted as deduplicatedpages in the page storage system 2506. Note that pre-deduplication pages3145 can also be stored in the same or different portions of the pagestorage system 2506, but can be denoted as pages requiringdeduplication. For example, page metadata 3115 can indicate whether agiven page has undergone deduplication by denoting each page as either apre-deduplication page 3145 or a deduplicated page 3155.

In some cases, only pages that have been deduplicated as deduplicatedpages 3155 are durably stored and/or have their records read in queryexecutions and segment generation. For example, queries are onlyexecuted on pages 2515 that have been updated as deduplicated pages3155. As another example, only pages that have been updated asdeduplicated pages 3155 are stored in page storage 2546 of one or morelong-term storage 2540. As another example, segments are only generatedfrom pages that have been updated as deduplicated pages 3155, where theconversion page set 2655 includes only deduplicated pages 3155. In somecases, pre-deduplication pages 3145 are only stored in page cache 2512.In some cases, pre-deduplication page 3145 are stored page storagesystem 2506, and are not moved from their location in page storagesystem 2506 once deduplicated as deduplicated pages 3155 to minimizedata movement.

When a new page 2515 is generated and/or initially stored as apre-deduplication page 3145, the row deduplication module candeduplicate the new page by identifying overlapping row numbers 3012with other pages 2515. This can be achieved based on comparing the rownumber span data 3119 of page metadata 3115 of the new page 2515 withthe row number span data 3119 in page metadata of other pages 2515, perdata source identifier 3014. The identified overlapping row numbers ofthe new page can be removed, for example, via updates to the new page'smetadata 3115, such that the duplicate rows are ignored in the new page,and are not ignored in the older pages. As illustrated in FIG. 28B, atleast one record 2422, and/or at least one set of records in particularrow data 2910, are removed from the pre-deduplication page 3145 torender the deduplicated page 3155, as denoted by the X over particularrow data 2910. Note that this row data 2910 is not necessarily deletedfrom the page 2515, but is denoted as removed row data that will not beread in page reads to the deduplicated page 3155.

Pages can be deduplicated as they are generated based on comparisonswith other, previously deduplicated pages, such as some or all pagesbeing accumulated in a same conversion page set 2655. In such cases,page deduplication can be performed by the page generator 2511 to enablededuplication of pages in conjunction with being generated. Pagededuplication can optionally be performed at any later time, forexample, prior to the conversion process of the corresponding pages2515. In some cases, some or all pages 2515 in conversion page set 2655optionally can be deduplicated in conjunction with the segmentgeneration, for example, where the segment generator 2517 implements therow deduplication module 30550 upon some or all pages in the pages 2515in conversion page set 2655 via comparisons with other pages inconversion page set 2655. In such cases, the segment generator 2517 canoptionally implements the row deduplication module 3050 afterdetermining to initiate the conversion process and/or prior toclustering records 2422 into record groups 2625.

The process of deduplicating a given page 3145 is illustrated in FIG.28C. FIG. 28C illustrates an example pre-deduplication page 3145-A thatis deduplicated to render a deduplicated page 3155-A. The rowdeduplication module 3050 of FIG. 28C can be utilized to implement therow deduplication module 3050 of FIG. 28A and/or any other embodimentsof the row deduplication module 3050 described herein.

As illustrated in FIG. 28C, page 3145-A has row number span data 3119for some or all data source identifiers 3014-1-3014-L. Each row numberspan data 3119 is reflected as a span from an original minimum rownumber 3117 to an original maximum row number 3118. Note that theparticular row numbers and/or the number of records included within eachspan are necessarily known and/or stored in page metadata 3115, as theyare not necessary in performing this processed and need not be indicatedin the metadata. Each row number span data 3119 therefore simply denotessome continuous interval within an entire row number domain 3160 of thecorresponding data source identifier 3014. A possible row number 3012that is included within particular row number span data 3119 thereforemay not have corresponding row data 2910 included in the correspondingpage, despite being included within the row number span data 3119. Thismay often be the case due to data sources generating row numbers 3012 atvarying, unknown intervals, for example, based on the row transmittalrequirement data only requiring that the row numbers 3012 strictlyincrease over time.

Other pages 2515, such as one or more deduplicated pages 3155 that havealready been deduplicated, can have their minimum row numbers andmaximum row numbers compared to the given page 3145-A's correspondingoriginal minimum row number and original maximum row number forcorresponding data sources. Note that some of all of these other pagesbeing compared to the given page 3145-A have already been deduplicated.Therefore, some of their minimum row numbers and maximum row numbers ofthese pages 3155 may correspond to previously updated minimum rownumbers and updated maximum row numbers due to the prior deduplicationof these pages 3155, while others of their minimum row numbers andmaximum row numbers may correspond to original minimum row numbers andoriginal maximum row numbers that didn't need modification in thededuplication these pages 3155.

All pages with row number span data 3119 that overlaps with one or moreof page 3145-A's row number span data 3119 can be identified. In thisexample, a plurality of overlapping deduplicated pages 3155 areidentified that include at least deduplicated page 3155-B anddeduplicated page 3155-C. Page 3145-A is deduplicated to renderdeduplicated page 3155-A based on removing portions of row number spandata 3119 of Page 3145-A that overlap with the row number span data inthis identified plurality of deduplicated pages 3155.

Deduplicated page 3155-B is identified based on having row number spandata 3119-1 that overlaps with page 3145-A's row number span data3119-1. This can be determined based on comparing page 3145-A's minimumrow number 3117-1 and/or maximum row number 3118-1 with page 3155-B'sminimum row number 3117-1 and/or maximum row number 3118-1. For example,page 3155-B is identified based on determining page 3155-B has a minimumrow number 3117-1 that is less than the pre-deduplication page 3145-A'smaximum row number 3118-1 and that is greater than the pre-deduplicationpage 3145-A's minimum row number 3118-1. The interval of row number spandata 3119 for data source identifier 3014-1 defined by the span betweenthe minimum row number 3117-1 of page 3155-B and the maximum row number3118-1 of page 3145-A is removed from page 3145-A in deduplication ofpage 3145-A. This can include generating updated maximum 3118-1 based onminimum row number 3117-1 of page 3155-B. For example, the updatedmaximum row number 3118-1 indicates that the row number span data 3119includes row numbers up to, but not including, minimum row number 3117-1of page 3155-B. As another example, the updated maximum row number3118-1 is set as a greatest possible row number 3012 of row numberdomain 3160 that is strictly less than minimum row number 3117-1 of page3155-B. As another example, the updated maximum row number 3118-1 is setas a greatest row number 3012 with row data 2910 included in theoriginal page 3145-A that is strictly less than minimum row number3117-1 of page 3155-B. This updated maximum row number 3118-1 canreplace the original maximum row number 3118-1 in the corresponding pagemetadata 3115 and/or can otherwise be indicated in the correspondingpage metadata 3115, where reads to deduplicated page 3155-A areperformed based on this updated maximum row number 3118-1.

Deduplicated page 3155-C is identified based on having row number spandata 3119-1 that overlaps with page 3145-A's row number span data 3119-1and based on further having row number span data 3119-L that overlapswith page 3145-A's row number span data 3119-L. The overlap of page3155-C's row number span data 3119-1 with row number span data 3119-1 ofpage 3145 can be determined based on comparing page 3145-A's minimum rownumber 3117-1 and/or maximum row number 3118-1 with page 3155-C'sminimum row number 3117-1 and/or maximum row number 3118-1. For example,page 3155-C is identified based on determining page 3155-C has a maximumrow number 3118-1 that is greater than the pre-deduplication page3145-A's minimum row number 3117-1 and that is less than thepre-deduplication page 3145-A's maximum row number 3118-1. The intervalof row number span data 3119 for data source identifier 3014-1 definedby the span between the minimum row number 3117-1 of page 3145-A and themaximum row number 3118-1 of page 3155-C is removed from page 3145-A indeduplication of page 3145-A. This can include generating updatedminimum row number 3117-1 based on maximum row number 3118-1 of page3155-C. For example, the updated minimum row number 3117-1 indicatesthat the row number span data 3119 includes row numbers down to, but notincluding, maximum row number 3118-1 of page 3155-C. As another example,the updated minimum row number 3117-1 is set as a lowest possible rownumber 3012 of row number domain 3160 that is strictly greater thanmaximum row number 3118-1 of page 3155-C. As another example, theupdated minimum row number 3117-1 is set as a lowest row number 3012with row data 2910 included in the original page 3145-A that is strictlygreater than maximum row number 3118-1 of page 3155-C. This updatedminimum row number 3117-1 can replace the original minimum row number3117-1 in the corresponding page metadata 3115 and/or can otherwise beindicated in the corresponding page metadata 3115, where reads todeduplicated page 3155-A are performed based on this updated minimum rownumber 3117-1.

The overlap of page 3155-C's row number span data 3119-L with row numberspan data 3119-L of page 3145 can be determined in a similar fashion.The interval of row number span data 3119 for data source identifier3014-L defined by the span between the minimum row number 3117-L of page3145-A and the maximum row number 3118-L of page 3155-C is similarlyremoved from page 3145-A in deduplication of page 3145-A. This caninclude similarly generating updated minimum row number 3117-L based onmaximum row number 3118-L of page 3155-C. This updated minimum rownumber 3117-L can similarly replace the original minimum row number3117-L in the corresponding page metadata 3115 and/or can otherwise beindicated in the corresponding page metadata 3115, where reads todeduplicated page 3155-A are performed based on this updated minimum rownumber 3117-L.

Other row number span data 3119 of other data source identifiers cansimilarly be updated, for example, based on identified overlap withother deduplicated pages 3155. Note that some minimum row numbers 3117and/or maximum row numbers 3118 may remain unaltered. For example, thededuplicated page 3155-A maintains original maximum row number 3118-L.While not depicted, some entire row number span data 3119 ofpre-deduplication page 3145-A for some data source identifiers 3014 canremain unaltered in deduplicated pages 3155-A based on not overlappingwith the corresponding row number span data 3119 of any other pages3155.

As reads are performed on deduplicated pages 3155, only records 2422 ofrow data 2910 with corresponding row numbers 3012 that fall within theupdated row number span data for the corresponding data sourceidentifier 3014 are read. Records 2422 of row data 2910 withcorresponding row numbers 3012 that do not fall within the updated rownumber span data for the corresponding data source identifier 3014 arenever read. This can be based on comparing row numbers 3012 with theminimum row number 3117 and maximum row number 3118 resulting fromdeduplication of the given deduplicated page 3155, whether or originalor updated, to determine whether a corresponding record 2422 ofcorresponding row data 2910 is to be returned in the read or is to beignored in the read.

Performing this process on all new pages as they are generated ensuresthat any given row data 2910 is not included in more than onededuplicated page 3155 based on removal of row number span overlap withpreviously deduplicated pages in this fashion. Performing this processfurther ensures that any given row data 2910 that is stored will not beremoved from all pages in page storage system, as the row number spanoverlap is not removed from the previously deduplicated pages. Thisrenders no duplication of any records in the deduplicated pages 3155 andin the resulting segments 2424 and further renders no unintentionaldeletion of records in the deduplicated pages 3155 and in the resultingsegments 2424 to guarantee the “exactly once” record storage indeduplicated pages 3155 and in the resulting segments 2424.

FIG. 28D illustrates an example embodiment where the row deduplicationmodule 3050 is implemented on individual nodes 37, for example, inconjunction with generation of pages by individual nodes 37 viaimplementing their own page generators 2511. An individual rowdeduplication module 3050 of an individual node 37 can be utilized toimplement the row deduplication module 3050 of FIG. 28A. The collectiveset of row deduplication modules 3050 of set of nodes 37-1-37-J can beutilized to implement the row deduplication module 3050 of FIG. 28A.

In some cases, each nodes 37 depicted in FIG. 28D can be utilized toimplement a corresponding steam loader module 2510 of FIG. 25B, whereeach steam loader module 2510 implements its row deduplication module3050 in conjunction with generation of its pages 2515 via its pagegenerator 2511. In some cases, the set of nodes 37-1-37-J are allincluded in a same storage cluster. In such cases, local page storage3146 of a given node 37 can be implemented utilizing the page cache 2512of the corresponding stream loader module 2510.

In some cases, each nodes 37 depicted in FIG. 28D can be utilized toimplement a corresponding long term storage 2540 of FIG. 25B, where eachlong term storage 2540 implements a row deduplication module 3050 uponpages stored in its page storage 2546, for example, where local pagestorage 3146 of a given node 37 can be implemented utilizing the pagestorage 2546 of the corresponding long term storage 2540.

In some cases, the set of nodes 37-1-37-J are all included in a samestorage cluster. In such cases, local page storage 3146 of a given node37 can be implemented utilizing the page cache 2512 of the correspondingstream loader module 2510. The set of nodes 37-1-37-J can otherwise beoperable to communicate data with each other and/or store data viacommon resources, for example, by utilizing system communicationresources 14. Local page storage 3146 of each node 37-1-37-J can each beincluded in page storage system 2506.

As different nodes 37 independently generate and deduplicate pages 2515,their deduplicated pages 3155 can be shared with other nodes 37. Inparticular, if a set of nodes 37-1-37-J are all receiving and generatingpages from row data 2910 of at least one same data source 2501, somelevel of data passing and/or storage of pages via commonly accessibleresources is required across the set of nodes 37-1-37-J. When a givennode 37 deduplicates a given page, such as newly generated page 2515, itcan identify page overlaps by performing the necessary metadatacomparisons with metadata 3115 of other pages 3155 generated by thegiven node and also metadata 3115 of other pages 3155 generated by othernodes in the set of nodes.

This can be achieved via a given node storing pages 2515 and/or theircorresponding page metadata 3115 in commonly accessible resources ofpage storage system 2506, where all other nodes in the set of nodes canaccess the stored pages 2515 and/or corresponding page metadata 3115generated by the given node via accessing the in commonly accessibleresources of page storage system 2506. The system communicationresources 14 can be utilized to store pages 2515 and/or theircorresponding page metadata 3115 in these common storage resourcesand/or to retrieve pages 2515 and/or their corresponding page metadata3115 from these common storage resources. In other cases, nodes can sendpages 2515 and/or their corresponding page metadata 3115 to other nodes37 in the page set directly via communications between the nodes 37 viasystem communication resources 14. As illustrated in the embodiment ofFIG. 28D, because only page metadata 3115 of deduplicated pages isnecessary in deduplicated in a given page, only the deduplicated pagemetadata is shared and/or accessed across the set of nodes in performingtheir deduplication of pages.

For example, as illustrated in FIG. 28D, a first node 37-1 utilizes itsrow deduplication module 3050 to perform deduplication of a particular,pre-deduplication page 3145-A to render deduplicated page 3155-A, forexample, as discussed in conjunction with FIGS. 28B and 28C. The pagemetadata 3115 of pre-deduplication page 3145-A is compared with pagemetadata 3115 in deduplicated page metadata set 1, which can beretrieved and/or accessed in its local page storage 3146, wherededuplicated page metadata set 1 corresponds to page metadata 3115 of aset of pages previously generated and/or deduplicated by the node 37-1.The page metadata 3115 of pre-deduplication page 3145-A is also comparedwith page metadata 3115 in deduplicated page metadata sets 2-J for pages2515 previously generated by other nodes 37-2-37-J. Once deduplicatedpage 3155-A is generated from pre-deduplication page 3145-A, it can bestored in local page storage 3146 and/or can have its updated pagemetadata 3115 sent to other nodes and/or sent to common storageaccessible to other nodes in subsequent deduplicate page metadata sharedby node 37-1. Note that received deduplicated page metadata sets 2-J canbe stored in node 37-1's local page storage 3146 and/or in other memoryof node 37-1 to enable node 37-1 to easily access page metadata 3115included in deduplicated page metadata sets 2-J for deduplication ofsubsequently processed pages 3145 by row deduplication module 3050.

Other nodes similarly utilize their own row deduplication modules 3050in this fashion to perform deduplication of pages 3145 based on metadatacomparisons with metadata included in the full, shared plurality of pagemetadata 3115 included in page metadata set 1-J. For example, node 37-Jsimilarly compares page metadata 3115 of pre-duplication page 3145-Bwith page metadata 3115 in its own deduplicated page metadata set J, andwith page metadata 3115 in deduplicated page metadata sets 1-J−1 forpages 2515 previously generated by other nodes 37-1-37-J−1

In some cases, the nodes 37-1-37-J can perform additional coordinationto ensure that pages generated within a same time frame by differentnodes, whose metadata was not utilized by other nodes 37 to generatetheir pages in this timeframe, are further deduplicated. In some cases,deduplication is later performed via comparisons of these pagesgenerated within this timeframe. In other cases, coordination and/orscheduling between nodes is facilitated via system communicationresources 14 utilized to ensures that this simultaneous deduplication bydifferent nodes 37 is not performed.

As a particular example, page 3145-A of FIG. 28D can correspond to page3145-A of FIG. 28C, and page 3155-B of FIG. 28D can correspond to page3155-B of FIG. 28C, where the process of FIG. 28C is performed by therow deduplication module 3050 of node 37-1, and where the process ofFIG. 28C includes receiving the page metadata of page 3155-B from node37-J in a deduplicated page metadata set J based on node 37-J havingalready generated page 3155-B. In some cases, the process of FIG. 28Cincludes receiving the page metadata of page 3155-C from local pagestorage based on node 37-1 having already generated page 3155-C.

In various embodiments, a record processing and storage system includesat least one processor and memory. The memory stores operationalinstructions that, when executed by the at least one processor, causethe record processing and storage system to receive, from a data source,a plurality of records and a plurality of row numbers corresponding tothe plurality of records. A plurality of pages is generated from theplurality of records. Each of the plurality of pages can include aproper subset of the plurality of records. Page metadata is generatedfor each of the plurality of pages that includes row number span datacorresponding to the data source based on a proper subset of theplurality of row numbers corresponding to the proper subset of theplurality of records included in the each of the plurality of pages. Aplurality of pairs of pages are identified in the plurality of pagesbased on having corresponding row number span data for the data sourcein their page metadata that include a row number span overlap. For eachpair in the plurality of pairs of pages, updated row number span data isgenerated for a first page in the pair by removing the row number spanoverlap with the row number span data of a second page in the pair fromthe row number span data of the first page in the pair, and the rownumber span data of the first page in the each pair is updated as theupdated row number span data. A plurality of reads of the plurality ofpages are performed based on the row number span data of the pluralityof pages. Only ones of the proper subset of the plurality of records ofeach first page of each pair in the plurality of pairs of pages havingcorresponding row numbers that are within the updated row number spandata are read in the performing of the plurality of reads.

FIG. 28E illustrates a method for execution by a record processing andstorage system 2505. For example, the database system 10 can utilize atleast one processing module of one or more nodes 37 of one or morecomputing devices 18, where the one or more nodes execute operationalinstructions stored in memory accessible by the one or more nodes, andwhere the execution of the operational instructions causes the one ormore nodes 37 to execute, independently or in conjunction, the steps ofFIG. 28E. For example, some or all of the method of FIG. 28E can beperformed via the set of nodes 37-1-37-J of FIG. 28D. Some or all of themethod of FIG. 28E can be performed by the page generator 2511 and/orthe page storage system 2506 of FIG. 25A. Some or all of the method ofFIG. 28E can be performed by one or more stream loader modules 2510 ofFIG. 25B, independently or in conjunction. Some or all of the method ofFIG. 28E can be performed by the row deduplication module 3050 of FIG.28B, FIG. 28C, and/or FIG. 28D. Some or all of the method of FIG. 28Ecan be performed by one or more of a plurality of row deduplicationmodules 3050 implemented by a plurality of nodes 37. Some or all of thesteps of FIG. 28E can optionally be performed by any other processingmodule of the database system 10. Some or all of the steps of FIG. 28Ecan optionally be performed by one or more data sources 2501, forexample, by utilizing the row transmission module 2706, and/or can beperformed via communication with one or more data sources 2501. Some orall of the steps of FIG. 28E can be performed to implement some or allof the functionality of the record processing and storage system 2505 ofFIG. 25A and/or FIG. 25B. Some or all of the steps of FIG. 28E can beperformed to implement some or all of the functionality of the rowdeduplication module 3050 of FIG. 28B, FIG. 28C, and/or FIG. 28D. Someor all steps of FIG. 28E can be performed by database system 10 inaccordance with other embodiments of the database system 10 and/or nodes37 discussed herein.

Step 2882 includes receiving a plurality of records and a plurality ofrow numbers. For example, the plurality of records is received from adata source, such as a data source 2501. For example, the plurality ofrecords as records 2422 included in one or more row data 2910 receivedfrom the data source over time. The plurality of row numbers cancorrespond to the plurality of records. Each record, and/or each batchof records in same row data 2910, can be assigned a unique row number bythe data source, where each row number is included in a transmissionpacket and/or otherwise received in conjunction with the correspondingone or more records. In various embodiments, the plurality of records istransmitted by the data source in accordance with a sequence. Anordering of the plurality of row numbers are in accordance with thesequence. For example, the row numbers can be strictly increasing valuesin accordance with an ordering of the plurality of records sent by thedata source. Any records that are retransmitted by the data sourcemultiple times keep their original row number to distinguish multipleinstances of these records received by the data source 2501 asduplicated records. The data source can send the row data 2910 asdiscussed in conjunction with one or more embodiments of FIGS. 27A-27G.

Step 2884 includes generating a plurality of pages from the plurality ofrecords, for example, via page generator 2511. For example, each of theplurality of pages includes a proper subset of the plurality of records.Each received record can be included in exactly one page. However, if arecord is received as a duplicated record in multiple transmissions fromthe data source, each instance of the duplicated record can be includedin different ones of the plurality of pages. The plurality of pages canbe stored in a page storage system, such as page storage system 2506.

Step 2886 includes generating page metadata for each of the plurality ofpages that includes row number span data. The row number span data foreach page can indicate and/or can be generated based on a proper subsetof the plurality of row numbers that correspond to the proper subset ofthe plurality of records included in the page. The row number span datacan correspond to the data source. For example, where a page thatincludes records received from multiple different data sources can haveseparate row number span data in the page metadata corresponding foreach of these different data sources. In various embodiments, the rownumber span data can include the minimum row number and the maximum rownumber of all row numbers for all records included in the page, such asthe minimum row number 3117 and the maximum row number 3118. Forexample, the row number span data indicates only this minimum row numberand this maximum row number, without indicating any other row numbersfor other records included in the page that are between minimum rownumber and this maximum row number. The page metadata of a given pagecan be included within the given page and/or can be mapped to the givenpage in the page storage system 2506.

Step 2888 includes identifying a plurality of pairs of pages in theplurality of pages having corresponding row number span data for thedata source in their page metadata that include a row number spanoverlap. Step 2890 includes, for each pair of the plurality of pairs ofpages, generating updated row number span data for a first page in eachpair by removing the row number span overlap with the row number spandata of a second page in pair from the row number span data of the firstpage in each pair. Step 2892 includes, for each pair of the plurality ofpairs of pages, updating the row number span data of the first page ineach pair as the updated row number span data generated for each pair instep 2890. For example, steps 2888, 2890, and/or 2892 are performed byutilizing one or more row deduplication modules 3050.

For example, the first page in each given pair can be implemented as apre-deduplication page 3145 of FIG. 28B and/or FIG. 28C, where thesecond page in each pair is implemented as a deduplicated page 3155stored in page storage system 2506, where the second page was previouslydeduplicated as a first page in a previously processed pair. Note that asame first page can be included in multiple pairs, for example, wherethe first page in a first pair and a second pair is a same page 3145such as page 3145-A of FIG. 28C, where the second page in the first pairis a first deduplicated page 3155, such as page 3155-B of FIG. 28C, andwhere the second page in the second pair is a second deduplicated page3155, such as page 3155-C of FIG. 28C.

Step 2894 includes performing a plurality of reads of the plurality ofpages based on the row number span data of the plurality of pages. Inperforming the plurality of reads, only ones of the proper subset of theplurality of records of each first page of each pair in the plurality ofpairs of pages having corresponding row numbers that are within theupdated row number span data are read in the performing of the pluralityof reads.

In various embodiments, at least one record in the proper subset of theplurality of records of each first page of each pair in the plurality ofpairs of pages are not read in the performing of the plurality of readsbased on these records having corresponding row numbers that are notwithin the updated row number span data of the each first page. Forexample, these records are skipped over and/or ignored in reading someor all of the plurality of records of the corresponding page based onhaving row numbers that do not fall within the updated row number spandata. In various embodiments, the corresponding row numbers of theseignored records that are not read in the performing of the plurality ofreads were within the original row number span data of the each firstpage prior to being updated as the updated row number span data, forexample, based on being included in the corresponding page when thecorresponding page was generated in step 2886 and prior to the rowdeduplication of steps 2888, 2890, and/or 2892.

In various embodiments, one page in each of the plurality of pairs ofpages has a minimum row number in the row number span data of its pagemetadata that is less than the maximum row number in the row number spandata of the page metadata for the other page in the each of theplurality of pairs of pages. For example, each of these pairs of pagesare identified as having a row number span overlap based on one page'sminimum row number being less than the other page's maximum row number.

In various embodiments, generating the updated row number span dataincludes selecting the first page in each pair based on predeterminedcriteria. In various embodiments, the predetermined criteria forselecting the first page in each pair that has its row number span datamodified is based on determining the page in the pair with a lowerminimum row number. For example, the first page can be selected in eachpair based on having a lower minimum row number than the other, secondpage in the pair, where the page with the lower minimum row number hasits row number span data updated. In this case, generating the updatedrow number span data further includes generating an updated maximum rownumber for the first page based on the minimum row number of the secondpage in the pair. For example, the updated maximum row number isgenerated to be equal to or based on a greatest row number of records inthe first page that is less than the minimum row number of the secondpage. The maximum row number of the row number span data for the firstpage is replaced with the updated maximum row number in generating theupdated row number span data. For example, the updated row number spandata indicates the updated maximum row number and the same minimum rownumber as the original row number span data.

In various embodiments, the predetermined criteria for selecting thefirst page in each pair that has its row number span data modified isbased on determining the page in the pair that was generated mostrecently. For example, the first page in each pair is selected basedbeing a more recently generated page than the other, second page in thepair. In this case, generating the updated row number span data furtherincludes selecting an updated maximum row number for the first pagebased on a greatest row number of records in the first page that is lessthan the minimum row number of the second page in the each pair, and/oror selecting an updated minimum row number for the first page based on alowest row number of records in the first page that is greater than themaximum row number of the second page in the each pair. For example,whether an updated maximum or updated minimum is selected is based onwhether the page's upper-end row number overlap with the other page'srow number span data or whether the whether the page's lower-end rownumber overlap with the other page's row number span data. The maximumrow number of the row number span data for the first page is replacedwith the updated maximum row number in the updated row number span datawhen an updated maximum row number was generated. The minimum row numberof the row number span data for the first page is replaced with theupdated minimum row number in the updated row number span data when anupdated minimum row number was generated.

In various embodiments, the predetermined criteria for selecting thefirst page in each pair that has its row number span data modified isbased on determining the page with a higher proportion of its pagesoverlapping, for example, by identifying the page with a larger numberof rows. The page with a larger number of rows can be selected as thefirst page to be modified, for example, as the larger page already hasmany other rows to be read even if its row number span data is updated.The page with a smaller number of rows can alternatively be selected asthe first page to be modified.

In various embodiments, plurality of records includes a set ofduplicated records. For example, the plurality of records includes theset of duplicated records based on the data source sending eachduplicated record in the set of duplicated records in multipletransmissions. A given duplicated record in the set of duplicatedrecords is included in the first page and the second page of a given oneof the plurality of pairs of pages. Performing the plurality of readsincludes reading records included in the first page and the second page.Exactly one instance of the given duplicated record is read in theperforming of the plurality of reads based on the updated row numberspan data of the first page. For example, the exactly one instance ofthe duplicated record is read in the performing of the plurality ofreads further based on the duplicated record being assigned a same rownumber by the data source in each one of its multiple transmissions.

The exactly one instance of the duplicated record is read, for example,based on this assigned row number of the duplicated record not beingincluded in the first page's updated row number span data. For example,this assigned row number of the duplicated record is not included in thefirst page's updated row number span data based on removal of theoverlapping span with the second page that includes this row number ofthe duplicated record. Thus, the given duplicated record is read fromthe second page and not the first page. The given duplicated record mayhave been written to one or more other pages as well, but is similarlynot read from these other pages based on their row number span data alsobeing updated to no longer include the row number of the duplicatedrecord. Exactly one instance of the other duplicated records in the setof duplicated records can similarly be read in performing the pluralityof reads based on other row number span data having been updated.

In various embodiments, the method includes generating a plurality ofsegments from a set of records read in the plurality of reads of theplurality of pages. The plurality of pages can include at least oneduplicated record, and the set of records included in the plurality ofsegments are is equivalent to a union distinct of the set of recordsincluded in the plurality of pages based on the updated row number spandata generated for the plurality of pairs of pages in the plurality ofpages. For example, all records are read from the plurality of pages,except for duplicated records which are ignored or skipped over based onthe row number span data being updated to updated row number span datafor at least some of the plurality of pages that include instances ofduplicated records.

In various embodiments, a first subset of the plurality of records arereceived from the data source in a first time frame. A first subset ofthe plurality of pages are generated from the first subset of theplurality of records. A second subset of the plurality of records arereceived from the data source after the first time frame. A new page isgenerated from the second subset of the plurality of records. A pair ofpages in the plurality of pairs of pages is identified that includes thenew page and an old page from the first subset of the plurality ofpages. The method includes selecting the new page as the first page ofthe pair of pages based on being generated after the old page. Forexample, the new page is selected based on the predetermined criteriadictating that newer pages have their row number span data updated. Theupdated row number span data is generated for the new page based on thenew page being selected as the first page.

In various embodiments, a plurality of row confirmation data istransmitted to the data source based on generating the first subset ofthe plurality of pages. Each row confirmation data is generated based onthe proper subset of the plurality of row numbers for the proper subsetof the plurality of records included in at least one page of the firstsubset of the plurality of pages. For example, the row confirmation datais sent as notifications acknowledging the set of row numbers whoserecords were received and/or included in pages. The row confirmationdata can indicate some or all row numbers for records from the datasource successfully received and included in the first subset of theplurality of pages. The method can include receiving a duplicated recordin the second subset of the plurality of records. For example, a firstinstance of the duplicated record was transmitted by the data source inthe first subset of the plurality of records, and a second instance ofthe duplicated record was retransmitted by the data source in the secondsubset of the plurality of records. For example, the duplicated recordwas retransmitted by the data source based on the data sourcedetermining the row number for the duplicated record is not included inthe set of row numbers indicated by the row confirmation data and/or byotherwise determining the duplicated record was not successfullyreceived and/or included in a page based on the row confirmation data.The row number of this duplicated record can be included in the rownumber span data generated for the new page, for example, based on theduplicated record being written to the new page. The row number of theduplicated record is not included in the updated row number span datagenerated for the new page based on the old page including theduplicated record and having row number span data including the rownumber of the duplicated record.

In various embodiments, the second subset of the plurality of recordsare received in a second time frame. A third subset of the plurality ofrecords are received from the data source after the second time frame. Asecond new page is generated from the third subset of the plurality ofrecords. A second pair of pages in the plurality of pairs of pages isidentified that includes the second new page and the new page. Themethod can further include selecting the second new page as the firstpage of the pair of pages, for example, based on being generated afterthe new page. Second updated row number span data is generated for thesecond new page by removing the row number span overlap with the updatedrow number span data of the new page from the row number span data ofthe second new page. For example, the updated row number span datapreviously generated form the new page in its deduplication with the oldpage is utilized to dictate the overlap with the second new page becausethe updated row number span data replaced the new page's original rownumber span data.

In various embodiments, the records corresponding to any number ofdifferent data sources can be received, stored, and deduplicated in asimilar fashion as discussed and illustrated in conjunction with FIG.28E. The method can include receiving, from a second data source, asecond plurality of records and a second plurality of row numberscorresponding to the second plurality of records. The plurality of pagesis generated from the plurality of records and the second plurality ofrecords. A subset of the plurality of pages can each include at leastone record from the plurality of records and at least one record fromthe second plurality of records. The method can further includegenerating, for each of the subset of the plurality of pages, second rownumber span data for the second data source based on a proper subset ofthe second plurality of row numbers corresponding to the at least onerecord of the second plurality of records included in the each of thesubset of the plurality of pages. For example, the second row numberspan data includes only a minimum row number and a maximum row numberfor records of the second data source. The page metadata for each of thesubset of the plurality of pages further includes the second row numberspan data. The method includes identifying a second plurality of pairsof pages in the plurality of pages having corresponding second rownumber span data in their page metadata that include a row number spanoverlap. For each pair in the second plurality of pairs of pages, themethod includes generating second updated row number span data for afirst page in the each pair by removing the row number span overlap withthe second row number span data of a second page in the each pair fromthe second row number span data of the first page in the each pair. Themethod includes updating the second row number span data of the firstpage in each pair as the second updated row number span data. Inperforming the performing of the plurality of reads, only ones thesecond plurality of records in each first page of each pair in thesecond plurality of pairs of pages having corresponding row numbers thatare within the second updated row number span data are read.

In various embodiments, a particular page is included a first pair ofpages in the plurality of pairs of pages and is also included in asecond pair of pages of the second plurality of pairs of pages. Updatedrow number span data, corresponding to the data source, is generated forthe page based on the row number span data of the second page in thefirst pair of pages. Second updated row number span data, correspondingto the second data source, is also generated for the page based on thesecond row number span data of the second page in the second pair ofpages. The second page in the first pair of pages can be the same as ordifferent from the second page in the second pair of pages.

In various embodiments, the method includes receiving, from the datasource, a first data source identifier of the data source in conjunctionwith receiving the plurality of records. The method can further includereceiving, from the second data source, a second data source identifierof the second data source in conjunction with receiving the secondplurality of records. The row number span data is generated based on rownumbers of records received in conjunction with the first data sourceidentifier, and the second row number span data is generated based onrow numbers of records received in conjunction with the second datasource identifier.

In various embodiments, performing the plurality of reads of theplurality of pages includes performing a first record traversal a firstpage in a pair of pages in the plurality of pairs of pages. At least onerecord in the proper subset of the plurality of records of the firstpage is skipped in the first record traversal of the first page based onthe updated row number span data of the first page. Performing theplurality of reads of the plurality of pages can also include performinga second record traversal a second page in the pair of pages in theplurality of pairs of pages. No records in the proper subset of theplurality of records of the second page are skipped in the second recordtraversal of the second page based on the row number span data of thesecond page not having been updated. In some cases, at least one recordthat was read from the second page in performing the plurality of readsof was skipped in the first record traversal of the first page.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing module that includes aprocessor and a memory, cause the processing module to: receive, from adata source, a plurality of records and a plurality of row numberscorresponding to the plurality of records; generate a plurality of pagesfrom the plurality of records, wherein each of the plurality of pagesincludes a proper subset of the plurality of records; generating pagemetadata for each of the plurality of pages that includes row numberspan data corresponding to the data source based on a proper subset ofthe plurality of row numbers corresponding to the proper subset of theplurality of records included in the each of the plurality of pages; andidentifying a plurality of pairs of pages in the plurality of pageshaving corresponding row number span data for the data source in theirpage metadata that include a row number span overlap. The operationalinstructions, when executed by the processing module, further cause theprocessing module to generate, for each pair in the plurality of pairsof pages, updated row number span data for a first page in the each pairby removing the row number span overlap with the row number span data ofa second page in the each pair from the row number span data of thefirst page in the each pair; and to update, for each pair in theplurality of pairs of pages, the row number span data of the first pagein the each pair as the updated row number span data. The operationalinstructions, when executed by the processing module, further cause theprocessing module to perform a plurality of reads of the plurality ofpages based on the row number span data of the plurality of pages. Onlyones of the proper subset of the plurality of records of each first pageof each pair in the plurality of pairs of pages having corresponding rownumbers that are within the updated row number span data are read in theperforming of the plurality of reads.

FIGS. 29A-29B illustrates an embodiment of a row deduplication module3050 that implements a page set filtering module 3210 to reduce the setof pages upon which the comparisons of row number span data 3119 isperformed. Some or all features and/or functionality of embodiments ofrow deduplication module 3050 of FIGS. 29A and 29C can be utilized toimplement the row deduplication module 3050 of FIG. 27A, of FIGS.28A-28D, and/or any other embodiments of row deduplication module 3050described herein.

While the page deduplication of FIGS. 28A-28D reduces computations viasimple metadata comparisons of row number span data 3119, performingthese comparisons across all necessary other pages for a given page canstill be computationally complex. Additional complexity is added in whendeduplication is performed across nodes 37 as discussed in conjunctionwith FIG. 28D. To further reduce complexity in deduplication, theassumption that row duplication only occurs in infrequent and/oranomalous scenarios can be leveraged to perform a less-computationallyintensive first, pre-check comparison to determine whether or notoverlap could exist between a pair of pages. A second comparison toidentify where the overlap exists is only performed for a given pair ofpages if this first pre-check is satisfied. For example, this secondcomparison includes the comparisons of minimum row numbers 3117 andmaximum row numbers 3118 of a pair of row number span data 3119 asdiscussed in conjunction with FIG. 28C. While performing both the firstpre-check comparison and the second comparison to identify overlap wouldresult in higher computation if overlaps occurred frequently, theinfrequent occurrence of overlaps and/or the infrequent occurrence ofpossibility of overlap means that only the less-expensive pre-check isperformed in most cases, and the second comparison is performed rarely.This improves the technology of database systems by further increasingthe efficiency of page deduplication, which preserves processingresources of the record processing and storage system 2505, thusenabling higher rates of incoming records to be processed into pages toachieve a richer and/or denser set of data in database system 10.

FIG. 29A illustrates an example of deduplicating a given page 3145-Afrom a plurality of pages 3155 in a full page set 3205. First, a pageset filtering module 3210 can be applied perform a first comparison ofpages 3155 in the full page set 3205 with the given page 3145-A. Thiscan be achieved via performance of a potential-intersection detectionfunction 3212. Performing the potential-intersection detection functionupon a pair of pages can include comparing page metadata 3115 of thepair of pages. The potential-intersection detection function can beperformed for each page 3155-i in the full page set to determinepotential intersection data for each page 3155-i in the full page setindicating whether there is a potential intersection with each page3155-i and the given page 3145-A. In some cases, performing thepotential-intersection detection function does not include anycomparisons of minimum row numbers 3117 or maximum row numbers 3118 of agiven pair of pages. An example embodiment of the potential-intersectiondetection function is discussed in further detail in conjunction withFIG. 29B.

If the potential intersection data indicates a given page 3155 has apotential intersection with page 3145-A, the given page 3155 is includedin a filtered page set 3215 identified by the page set filtering module3210. If the potential intersection data indicates a given page 3155does not have a potential intersection with page 3145-A, the given page3155 is not included in a filtered page set 3215 identified by the pageset filtering module 3210. The resulting filtered page set 3215 can be aproper subset of the full page set 3205 based on at least one page 3155of the full page set 3205 having potential intersection data indicatingthere is no potential intersection with page 3145-A.

An intersection removal module 3220 can be applied to only the pagesincluded in the filtered page set 3215. For each page 3155-i identifiedin the filtered page set 3215, a row intersection determination function3225 is utilized to determine row intersection data indicatingintersecting rows with page 3145-A. Applying the row intersectiondetermination function 3225 can include performing a second comparisonof each page 3155-i and the given page 3145-A. For example, this secondcomparison can include comparing row number span data 3119 of a givenpair of pages to generate the row intersection data identifying whetheran overlap is present and/or to further identifying the location of theoverlap in the row number span data 3119 of the given page 3145-A, ifpresent. As a particular example, applying the row intersectiondetermination function 3225 can include comparing minimum row numbers3117 and maximum row numbers 3118 of a given pair of pages in row numberspan data for each shared data source identifier 3014 of the pair ofpages to determine overlapping portions of row number span data 3119 asdiscussed in conjunction with FIG. 28C. This second comparison of therow intersection determination function 3225 can alternatively utilizeother means of identifying row intersections, and can optionally includeidentifying particular records 2422 that are duplicated in a given page3155 and in page 3145-A. This can optionally include utilizing differentinformation in labeled row data 3010 and/or in page metadata 3115 todetermine the row intersection data.

The intersection removal module 3220 can facilitate removal of recordsindicated in row intersection data for each page 3155-i from page 3145-Ato render deduplicated page 3155-A, for example, as discussed inconjunction with FIG. 28B. For example, the intersection removal module3220 can be implemented via the updating of row number span data 3119 ofpage 3145-A, which can include generating an updated minimum row number3117 and/or and updated maximum row number 3118 for page 3145-A asdiscussed in conjunction with FIG. 28C.

The computational intensity and/or resource requirements of performingthe potential-intersection detection function 3212 for a pair of pagescan be strictly less than the computational intensity and/or resourcerequirements of performing row intersection determination function 3225for a pair of pages. This is ideal, as only the filtered page set 3215for a given record need undergo the more computationally intensive rowintersection determination function 3225.

FIG. 29C illustrates an embodiment of a row deduplication module 3050that utilizes a matching source ID determination function 3242 toimplement the potential-intersection detection function 3212 of FIG.29A. For a given pair of pages, the matching source ID determinationfunction 3242 can be utilized to determine whether the pair of pagesshare any records from the same data source 2501, where the filteredpage set 3215 generated for a given page 3145-A includes only pages 3155that have at least one record generated by one of the data sources 2501that generated records included in the given page 3145-A. The set ofdata source identifiers 3014 with records included in page 3155-i and inpage 3145-A can be determined based on data source identifiers 3014 intheir respective page metadata 3115.

Other embodiments of the potential-intersection detection function 3212of FIG. 29A can alternatively or additionally include other types ofcomparisons, for example, of page metadata 3115 and/or of otherattributes of pages 2515. In some cases, filtered sets ofpotentially-intersecting pages can be tracked over time via rowdeduplication module 3050 to further simplify the computation requiredin performing potential-intersection detection function 3212. In suchcases, performing potential-intersection detection function 3212 for afull page set 3205 can optionally include accessing previously generatedpotential-intersection data stored in memory to identify the filteredpage set 3215. In some cases pages with particular data sourceidentifiers 3014 can be easily identifiable, for example, via a lookuptable maintained by the row deduplication module 3050. In such cases,performing potential-intersection detection function 3212 for a fullpage set 3205 can optionally include accessing a lookup table of datasource identifiers stored in memory to identify the filtered page set3215. In some cases, performing potential-intersection detectionfunction 3212 includes maintaining and/or accessing other tracked and/orstored data in memory, where generating filtered page set 3215 from afull page set 3205 optionally does not require accessing page metadataof some or all pages 3155 in the full page set 3205 based on insteadutilizing this tracked and/or stored data.

FIG. 29C illustrates a method for execution by a record processing andstorage system 2505. For example, the database system 10 can utilize atleast one processing module of one or more nodes 37 of one or morecomputing devices 18, where the one or more nodes execute operationalinstructions stored in memory accessible by the one or more nodes, andwhere the execution of the operational instructions causes the one ormore nodes 37 to execute, independently or in conjunction, the steps ofFIG. 29C. Some or all of the method of FIG. 29C can be performed by thepage generator 2511 and/or the page storage system 2506 of FIG. 25A.Some or all of the method of FIG. 29C can be performed by one or morestream loader modules 2510 of FIG. 25B, independently or in conjunction.Some or all of the method of FIG. 29C can be performed by the rowdeduplication module 3050 of FIG. 28B, FIG. 28C, FIG. 28D, FIG. 29A,and/or FIG. 29B. For example, some or all of the method of FIG. 29C canbe performed: by the page set filtering module 3210, for example, byperforming the potential-intersection detection function 3212 and/or thematching source ID determination function 3242; and/or by theintersection removal module 3220, for example, by implementing the rowintersection determination function 3225. Some or all of the method ofFIG. 29C can be performed by one or more of a plurality of rowdeduplication modules 3050 implemented by a plurality of nodes 37. Someor all of the steps of FIG. 29C can optionally be performed by any otherprocessing module of the database system 10. Some or all of the steps ofFIG. 29C can optionally be performed by one or more data sources 2501and/or can be performed via communication with one or more data sources2501. Some or all of the steps of FIG. 29C can be performed to implementsome or all of the functionality of the record processing and storagesystem 2505 of FIG. 25A and/or FIG. 25B. Some or all of the steps ofFIG. 29C can be performed to implement some or all of the functionalityof the row deduplication module 3050 of FIG. 28B, FIG. 28C, FIG. 28D,FIG. 29A, and/or FIG. 29B. Some or all steps of FIG. 29C can beperformed by database system 10 in accordance with other embodiments ofthe database system 10 and/or nodes 37 discussed herein.

Step 2982 includes storing a plurality of pages with corresponding pagemetadata in a page storage system. The method can further includereceiving a plurality of labeled row data from at least on data sourceand/or generating the plurality of pages from records included in thelabeled row data that include page metadata. The page metadata of agiven can indicate row number span data for at least one of theplurality of data sources based on row numbers of a set of recordsincluded in a given page. For example, the page metadata is generatedbased on row numbers and/or data source identifiers included inconjunction with the set of records in one or more corresponding labeledrow data.

Step 2984 includes facilitating deduplication of the plurality of pagesbased on their corresponding page metadata. Performing step 2894 caninclude performing for each of the plurality of pages, step 2986, step2988, and/or step 2990. In some cases, step 2986, step 2988, and/or step2990 are performed for a given page upon being generated and/or stored,for example, via comparisons with other pages already stored by the pagestorage system. In some cases, steps 2986, step 2988, and/or step 2990can be performed to implement performance of steps 2888, 2890, and/or2802 of FIG. 28E.

Step 2986 includes identifying, for each page, a filtered set ofpotentially-intersecting pages as a proper subset of the plurality ofpages stored in the page storage system based on first comparisonparameters. This can include by comparing, in accordance with the firstcomparison parameters, page metadata of the each of the plurality ofpages with the page metadata of other ones of the plurality of pagesstored in the page storage system. In some cases, the first comparisonparameters indicate comparison based on data source identifiers includedin the page metadata, for example, where the filtered set ofpotentially-intersecting pages for a given page includes only pages inthe plurality of pages stored in the page storage system with at leaston data source identifier in their metadata that matches a data sourceidentifier of the given page.

Step 2988 includes identifying, for each page, an intersecting set ofpages that include a row number intersection with the each of theplurality of pages as a proper subset of the first filtered set ofpotentially-intersecting pages based on second comparison parameters.For example, this can include comparing, in accordance with the secondcomparison parameters, page metadata of the each of the plurality ofpages with the page metadata of only ones of the plurality of pages infirst filtered set of potentially-intersecting pages. In some cases,this comparison via the second comparison parameters is not performed,for a given page, with on any pages that are not included in the givenpage's filtered set of potentially-intersecting pages identified in step2986. In some cases, the first comparison parameters indicate comparisonbased on row numbers included in the page metadata. In particular, therow number intersection can be determined based on comparing row numberspan data of pages' page metadata. For example, the row numberintersection is determined as the row number overlap discussed inconjunction with FIGS. 28A-28D based on only minimum row numbers andmaximum row numbers of pages' page metadata.

Step 2990 includes removing, from each page, records with row numbersincluded in the row number intersections with other pages in theintersecting set of pages. For example, the row number span data a givenpage's page metadata, such as a maximum row number and/or minimum rownumber of the page metadata, can be updated to remove the overlap withrow number span data of other pages as discussed in conjunction withFIGS. 28A-28D. In some cases, removal of the records does not includedeletion of the corresponding records from storage in the page, butinstead includes skipping and/or ignoring of these records during pagereads and/or page traversals. The method can further include performingreads to these pages, where any removed records are not returned and/orare not able to be read.

FIGS. 30A-30C present embodiments of a record processing and storagesystem that includes a plurality of storage clusters 35-1-35-z. Theplurality of storage clusters 35-1-35-z of FIGS. 30A-30C can beimplemented as the plurality of storage clusters 35-1-35-z of FIG. 6 .Each of the plurality of storage clusters 35-1-35-z can include a sameor different number of nodes 37. For example, each storage cluster 35can include at least a set of nodes 37-1-37-J corresponding to a numberof segments J included in a segment group in accordance with theredundancy storage coding scheme. One or more of the plurality ofstorage clusters 35 can be implemented via the storage cluster 2535 ofFIG. 25B. For example, each storage cluster 35-1-35-z can include and/orcommunicate with its own designated set of stream loader modules2510-1-2510-N of FIG. 25B.

Each of the plurality of storage clusters 35-1-35-z can otherwise beimplemented via any distinct set of nodes 37 that are operable tocommunicate data with one another; are operable to share commonprocessing and/or storage resources; and/or are operable to collectivelyperform page generation, page deduplication, and/or segment generationfor the corresponding storage cluster 35 via coordination between someor all of the set of nodes 37 in the corresponding storage cluster 35.In some cases, any pair of nodes 37 in different storage clusters 35 arenot operable to communicate data with one another, are not operable toshare common processing and/or storage resources; and/or are notoperable to collectively perform page generation, page deduplication,and/or segment generation via coordination between the pair of nodes 37.

In some cases, embodiments of the record processing and storage system2505 described herein, such as the record processing and storage system2505 of FIGS. 25A and/or 25B, are optionally implemented as one of aplurality of distinct record processing and storage systems 2505 ofdatabase system 10, where each record processing and storage system 2505corresponds to and/or is implemented by processing resources of acorresponding one of a plurality of storage clusters 35-1-35-z.Alternatively or in addition, embodiments of the record processing andstorage system 2505 described herein, such as the record processing andstorage system 2505 of FIGS. 25A and/or 25B, are implemented via aplurality of distinct record processing and storage systems 2505 ofdatabase system 10, where each record processing and storage system 2505corresponds to and/or is implemented by processing resources of acorresponding one of a plurality of storage clusters 35-1-35-z.

For example, each record processing and storage system 2505 canindependently generate pages 2515 from its own set of records 2422without coordination with other record processing and storage systems2505 of other storage clusters 35. In such cases, each record processingand storage system 2505 can receive its own distinct set of records2422, for example, as a distinct set of labeled row data 3010 from oneor more data sources 2501. In such cases, each data sources 2501 can bedesignated to send its records to a particular record processing andstorage system 2505 of a particular storage cluster 35. Alternatively, adata sources 2501 can be designated to send its records to a centralreceiving entity that services all record processing and storage systems2505 of a storage clusters 35, where the central receiving entitysegregates the received records 2422 and/or the received labeled rowdata 3010 for processing by a particular a particular record processingand storage system 2505 of a particular storage cluster 35. In somecases, a given data source 2501 can optionally have some of its recordsprocessed and generated into pages by one record processing and storagesystem 2505 of one particular storage cluster 35, and can have othersones of its records processed and generated into pages by another recordprocessing and storage system 2505 of another particular storage cluster35.

As another example, each record processing and storage system 2505 canindependently deduplicate pages 2515 based on comparisons performed withonly its own set of pages 2515, for example, via communication withinits own set of nodes 37-1-37-J as discussed in conjunction with FIG.28D, and/or without coordination with other record processing andstorage systems 2505 of other storage clusters 35. Despite deduplicationbeing handled without coordination across all nodes 37 receiving recordsand generating pages, deduplication can be performed appropriately todeduplicate all records by designation of each data source 2501'srecords for processing by exactly one storage cluster 35. This ensuresthat each storage cluster 35 has all potentially intersecting records inits own set of generated pages. Thus, deduplication by each storagecluster upon only its own set of pages is guaranteed to deduplicate allrecords across all storage clusters 35-1-35-z of the database system 10.The designation of each data source 2501's records to a particularstorage cluster 35 in the set of storage clusters 35-1-35-z is discussedin further detail in conjunction with FIGS. 30B and 30C.

As another example, each record processing and storage system 2505 canindependently perform the page conversion process by accumulating andconverting its own conversion page set 2655 of its own plurality ofgenerated pages into a plurality of segments 2424, without coordinationwith other record processing and storage systems 2505 of other storageclusters 35. This can optionally include performing the clusteringprocess to generate record groups 2625 only from records 2422 receivedand included in pages generated by the corresponding record processingand storage system 2505. Alternatively, despite distinct sets of pageseach being generated by, deduplicated within, and/or stored withindistinct storage clusters 35, to improve clustering of records insegments, the segment generator 2517 can perform the conversion processfrom a conversion page set that includes multiple distinct sets of pagesfrom different storage clusters 35 that were separately generated and/ordeduplicated.

Query processing system 2502 can optionally generate query executionplans data that includes nodes 37 included in only one storage cluster35. Query processing system 2502 can optionally generate query executionplans data that includes nodes 37 included across multiple storageclusters 35. For example, despite each storage cluster 35 independentlyperforming page deduplication and/or independently generating segmentsfrom its own set of pages, a particular query can be executed via accessto pages generated by and/or stored by multiple different storageclusters 35 and/or via access to segments generated by and/or stored bymultiple different storage clusters 35.

The embodiments of FIGS. 30A-30C can be utilized to implement separatededuplication of records by resources of each of the plurality ofstorage clusters 35-1-35-z separately. At scale, it can be unreasonableto facilitate the deduplication between nodes in the fashion discussedin conjunction with FIG. 28D via nodes across different storageclusters. To ensure that records are appropriately deduplicated,constraints can be put in place to ensure that all pages that include aparticular data source's records are stored by and/or deduplicated by aset of nodes 37 of the same storage cluster. This simplifies complexityof the deduplication process by localizing the deduplication process tofewer nodes, while still ensuring the deduplication process achieves“exactly once” record reads by guaranteeing the deduplication process isperformed across all nodes storing potentially intersecting pages.

In particular, the record processing and storage system 2505 of eachstorage cluster 35 can receive labeled row data 3010 from a distinct setof data sources 2501 with a distinct set of data source identifiers3014. The record processing and storage system 2505 of each storagecluster 35 can generate pages from the labeled row data 3010 from thisdistinct set of data sources 2501 and can further generate page metadata3115 as discussed in conjunction with FIGS. 28A-28C. The recordprocessing and storage system 2505 of each storage cluster 35 can storeits generated pages in its page storage system 2506, which can includethe local page storage systems 3146 the corresponding storage cluster35's set of nodes 37-1-37-J and/or can include other storage accessibleby the set of nodes 37-1-37-J, for example, where this page storagesystem 2506 is not accessible by other nodes 37 in other storageclusters 35.

The record processing and storage system 2505 of each storage cluster 35can deduplicate each generated page, for example, by converting theoriginal version of a given page 2515 as a pre-deduplication page 3145to a deduplicated page 3155 via comparisons of metadata 3115 of thepre-deduplication page 3145 with metadata of other deduplicated pages3155 as discussed in conjunction with FIGS. 28A-28C and/or inconjunction with FIGS. 29A-29B. In particular, a given node 37 of aparticular storage cluster can deduplicate a given page 3145 based onmetadata of other deduplicated pages 3155 generated by some or all nodes37 included in the same storage cluster 35, for example, as discussed inconjunction with FIG. 28D. However, a given node 37 of a particularstorage cluster only utilizes metadata of other deduplicated pages 3155generated by some or all nodes 37 included in the same storage cluster35 but does not utilize metadata of other deduplicated pages 3155generated by some or all nodes 37 included in different storage clusters35.

This can be achieved via designation of each data source 2501 to exactlyone storage cluster. This guarantees that, for a given page 3145generated by a given record processing and storage system 2505 of agiven storage cluster 35, all potentially intersecting pages generatedacross all storage clusters are localized to the same given storagecluster 35. This improves the technology of database systems byguaranteeing deduplication and thus guaranteeing all records are readfrom pages and/or resulting segments in query executions exactly once toguarantee query correctness. This further improves the technology ofdatabase systems by reducing the coordination required across thedatabase system to attain these “exactly once” reads of records acrossall storage clusters 35 in the database system. In particular, ratherthan requiring the sharing of page metadata between the vast number ofnodes 37 in the database system to achieve deduplication, page metadatasharing is localized to distinct groups of smaller numbers of nodes. Inthis fashion, smaller numbers of page metadata are required to becompared to a given page by a given node. Furthermore, a given page'smetadata is shared with a smaller number of nodes rather than all nodesin the database system 10. This localized deduplication reduces the datasharing and computational complexity of page deduplication to reduceprocessing resources required to perform page deduplication to improvedatabase efficiency. This reduction of processing resources for pagededuplication enables more processing resources to be utilized to enablereceiving and processing of higher rates of incoming records for storageto achieve a denser and/or richer database system 10.

As illustrated in FIG. 30B, the record processing and storage system2505 and/or another at least one processing module of the databasesystem 10 can implement a data source assignment module to generate datasource assignment data 3316. The data source assignment module 3305 cangenerate the data source assignment data 3316 map each of a set of datasources 2501-1-2501-L in a full data source set 3314 that send records2422 for storage in the database system 10 to a corresponding one of aplurality of storage clusters 35-1-35-z in a storage cluster set 3312 ofthe database system 10. This results in a plurality of data source sets3310-1-3310-z, where each data source sets 3310 corresponds to one ofthe storage clusters 35-1-35-z. Each data source set 3310 can include adistinct subset of data sources 2501 from the full data source set 3314.The plurality of data source sets 3310-1-3310-z can be mutuallyexclusive and/or collectively exhaustive with respect to the full datasource set 3314.

The data source assignment module 3305 can be operable to segregate thefull data source set 3314 into the plurality of data source sets3310-1-3310-z of the data source assignment data 3316 based on mappingparameters which can be: received by the data source assignment module3305; configured via user input; accessed by the data source assignmentmodule 3305 from storage in memory; automatically generated by the datasource assignment module 3305; and/or otherwise determined by the datasource assignment module 3305.

These mapping parameters can dictate that the full data source set 3314be segregated into the plurality of data source sets 3310-1-3310-z basedon: including an equal number of similar number of data sources 2501 ineach data source set 3310; determining a record size of records sent byeach data source and/or a record transmission rate of each data sourceand generating the plurality of data source sets 3310-1-3310-z toreceive incoming records at a same or similar incoming data rate basedon these determined record sizes and/or determined record transmissionrates; determining a storage capacity and/or resource availability ofeach of the plurality of storage clusters 35 and assigning generatingthe plurality of data source sets 3310-1-3310-z to have correspondingincoming data rates that are exactly and/or substantially proportionalto and/or otherwise based on the storage capacity and/or resourceavailability of each of the plurality of storage clusters 35;determining duplicated record rates of records received from differentdata sources over time and generating the plurality of data source sets3310-1-3310-z to have same and/or similar duplicated record rates and/orto be otherwise based on the determined duplicated record rates;determining and/or tracking a level of clustering and/or clusteringmetrics attained for records of different data sources 2501 in previousconversions processes of pages into segments and generating theplurality of data source sets 3310-1-3310-z based on the determinedand/or tracked level of clustering and/or clustering metrics; and/orbased on other mapping parameters.

The data source assignment module 3305 can optionally change the datasource sets 3310-1-3310-z over time by generating one or more updateddata source assignment data 3316 reassigning some or all data sources3314 to different storage clusters 35 from previous data sourceassignment data 3316. For example, these changes to the data source sets3310-1-3310-z can be determined based on: the set of storage clusters inthe storage cluster set 3312 changing; the set data sources in the fulldata source set 3314 changing; changes in record size and/or recordschema of one or more data sources 2501; changes in record transmissionrate of one or more data sources 2501; changes in storage capacity,resource availability, and/or resource health of one or more storageclusters 35; changes in clustering levels achieved in subsequentconversion processes for records of one or more data sources 2501;changes in duplication rate of records sent by one or more data sources2501; changes of other metrics relating to the mapping parametersutilized to generate the data source sets 3310-1-3310-z; and/or anotherdetermination.

In some cases, such assignment changes can be implemented to ensurepages will not need to be deduplicated across multiple storage clusters.This can include determining a segment generator 2517 of particularstorage cluster 35, and/or a segment generator 2517 across some or allstorage clusters 35, has recently converted and/or has recentlyinitiated the conversion process of pages into segments. For example, agiven storage cluster may begin to receive records from a given datasource based on these assignment changes, where no pages upon any otherstorage cluster currently include records of this new data source whenthe assignment change is implemented. This can be based on anotherstorage cluster to which the given data source was previously assignedhaving converted and/or being in the process of converting all of itspages that include the records of the given data source. This enablesdeduplication to be performed appropriately, while also enabling updatesto the data source assignment data 3316 to be implemented as appropriateover time.

FIG. 30C illustrates an embodiment of a record processing and storagesystem 2505 that implements separate page generators 2511 for each of aplurality of storage clusters 35-1-35-z. Each page generator 2511 ofeach storage clusters 35 only generates pages 2515 from labeled row data3010 with data source identifiers 3014 corresponding to data sources2501 assigned to a corresponding data source set 3310, for example, inaccordance with the data source assignment data 3316 generated by thedata source assignment module 3305 of FIG. 30B.

For example, storage cluster 35-1 processes all labeled row data 3010generated by data sources 2501 in a corresponding data source set2501-1, which includes at least a data sources 2501-1 and a data source2501-V. Each data source in each data source set 3310-1-3310-z generatesand transmits labeled row data 3010, for example, as discussed inconjunction with FIGS. 27A-27H. In particular, each labeled row data3010 can be generated to include a corresponding data source identifier3014 for the corresponding data source 2501 as discussed previously.

Each data source 2501 can optionally route its labeled row data 3010labeled row data 3010 to a corresponding storage cluster 35, forexample, by sending its labeled row data 3010 to a correspondingcomputing device of the utilized to implement the corresponding pagegenerator 2511 and/or utilized to route incoming data to thecorresponding page generator 2511. In such cases, the record processingand storage system 2505 can optionally transmit some or all of datasource assignment data 3316 to each data source 2501 and/or otherwiseindicate which storage cluster 35 their labeled row data 3010 is to berouted based on the data source assignment data 3316. Alternatively,data sources 2501 can send their labeled row data 3010 to a commonreceiving module of the record processing and storage system 2505, wherethis common receiving module utilizes the data source identifiers 3014included in the labeled row data 3010 to route the labeled row data 3010for processing by the appropriate page generator 2511 of the appropriatestorage cluster 35 in accordance with the data source assignment data3316.

Each page generator 2511 of each storage cluster 35 stores deduplicatedpages 3155 in its own distinct storage resources, for example,corresponding to its own distinct page storage system 2506 that isseparate from page storage systems 2506 of other storage clusters 35.Each page generator 2511 implements its own row deduplication module todeduplicate new, pre-deduplication pages 3145 based on comparisons withits own set of previously generated pages 3155 by its own page generator2511, for example, via access to these previously generated pages 3155in its own page storage system 2506. For example, each row deduplicationmodule 3050 of each storage cluster 35 is implemented via some or all ofthe features and/or functionality of embodiments of row deduplicationmodule 3050 of FIGS. 28A-28D and/or of FIGS. 29A-29B.

FIG. 30D illustrates a method for execution by a record processing andstorage system 2505. For example, the database system 10 can utilize atleast one processing module of one or more nodes 37 of one or morecomputing devices 18, where the one or more nodes execute operationalinstructions stored in memory accessible by the one or more nodes, andwhere the execution of the operational instructions causes the one ormore nodes 37 to execute, independently or in conjunction, the steps ofFIG. 30D. Some or all of the method of FIG. 30D can be performed by thepage generator 2511 and/or the page storage system 2506 of FIG. 25A.Some or all of the method of FIG. 30D can be performed by one or morestream loader modules 2510 of FIG. 25B, independently or in conjunction.Some or all of the method of FIG. 30D can be performed by the rowdeduplication module 3050 of FIG. 28B, FIG. 28C, FIG. 28D, FIG. 29A,FIG. 29B, FIG. 30A, and/or FIG. 30C. Some or all of the method of FIG.29C can be performed by one or more of a plurality of row deduplicationmodules 3050 implemented by a plurality of nodes 37. Some or all of themethod of FIG. 29C can be performed by computing devices 18 included ina particular storage cluster 35, where different computing devices 18included in different storage cluster 35 can independently perform someor all of the steps of FIG. 29C without coordination between storageclusters 35. Some or all of the method of FIG. 30D can be performed bythe data source assignment module 3305 of FIG. 30B. Some or all of thesteps of FIG. 30D can optionally be performed by any other processingmodule of the database system 10. Some or all of the steps of FIG. 30Dcan optionally be performed by one or more data sources 2501 and/or canbe performed via communication with one or more data sources 2501. Someor all of the steps of FIG. 30D can be performed to implement some orall of the functionality of the record processing and storage system2505 of FIG. 25A and/or FIG. 25B. Some or all of the steps of FIG. 30Dcan be performed to implement some or all of the functionality of therow deduplication module 3050 of FIG. 28B, FIG. 28C, FIG. 28D, FIG. 29A,FIG. 29B, FIG. 30A, and/or FIG. 30C. Some or all steps of FIG. 30D canbe performed by database system 10 in accordance with other embodimentsof the database system 10 and/or nodes 37 discussed herein.

Step 3082 includes receiving a plurality of records from a plurality ofdata sources. For example, some or all of the plurality of records arereceived as row data and/or labeled row data generated by some or all ofthe plurality of data sources. Step 3084 includes generating, for eachof a plurality of storage clusters, a subset of a plurality of pagesthat includes only ones of the plurality of records received from datasources included in a corresponding one of the plurality of data sourcesets. A plurality of subsets of the plurality of pages corresponding tothe plurality of storage clusters are generated. The plurality ofsubsets can be mutually exclusive and collectively exhaustive withrespect to the plurality of pages generated by the record processing andstorage system. Each subset can be generated by distinct processingresources, for example, where only computing devices 18 included in aparticular storage cluster 35 generate pages from data sources includedin a corresponding one of the plurality of data source sets. In suchcases, processing resources of different storage clusters 35 optionallyreceive only ones of the plurality of records generated by the datasources in the corresponding data source set. Alternatively, a commonset of processing resources, such as a set of computing devices 18across multiple storage clusters 35, can generate pages included inmultiple different subsets by ensuring each page includes only recordsfrom data sources in the same data source set.

The plurality of data source sets can be indicated by data sourceassignment data that maps each data source to a particular storagecluster in a plurality of storage clusters. In some cases, the methodfurther includes generating this data source assignment data bysegregating a plurality of data sources into a plurality of data sourcesets mapped to a corresponding plurality of storage clusters of therecord processing and storage system. In some cases, generating the datasource assignment data includes generating data sources sets with thesame or relatively equal numbers of data sources; generating datasources sets attaining relatively equal data input rates for eachstorage cluster based on determining data transmission rates of eachdata source; generating data sources sets to attain data ratesproportional to storage resource capacity and/or ingress resourceavailability of the different storage clusters; and/or generating datasources sets in accordance with other criteria based on the data sources2501 and/or based on the storage clusters 35. Alternatively, this datasource assignment data can be retrieved from memory, received via userinput, and/or otherwise determined. The plurality of data source setscan be mutually exclusive and collectively exhaustive with respect tothe full plurality of data sources. In some cases, the method furtherincludes sending the determined data source assignment data to theplurality of data sources. In such cases, a given data source can routetheir records to one or more computing devices 18 of a particularstorage cluster 35 based on determining assignment to the particularstorage cluster 35 in the received data source assignment data.

Step 3086 includes storing, for each of the plurality of storageclusters, the corresponding subset of the plurality of pages of pagesvia storage resources of the each of the plurality of storage clusters.This can render storage of the plurality of pages in the recordprocessing and storage system. The storage resources of the each of theplurality of storage clusters can be mutually exclusive with respect tostorage resources across all of the plurality of storage clusters, whereeach storage cluster has distinct storage resources. For example, thestorage resources of a given storage cluster are implemented byutilizing memory drives and/or cache memory of one or more one or morenodes 37 of one or more computing devices 18 of the given storagecluster.

Step 3088 includes facilitating for each of the plurality of storageclusters, deduplication of each page included in the correspondingsubset of the plurality of pages based on metadata comparisons withother pages included in the corresponding subset of the plurality ofpages. This can render deduplicating the entire plurality of pages bythe record processing and storage system. Deduplication of each pageincluded in a given storage cluster can include only comparing the pageto other pages in the same storage cluster 35, and not comparing thepage to pages in other storage clusters 35.

The method can further include, for each storage cluster, facilitatingreads to the pages after deduplication in step 3088. These canfacilitate execution of queries by utilizing deduplicated records storedin pages. The method can further include, for each storage cluster,generating a plurality of segments from its plurality of pages afterdeduplication in step 3088. In some cases, each of the plurality ofsegments are all in the same storage cluster that stored thecorresponding plurality of pages. In other cases, some segments are sentto and/or stored by other storage clusters.

FIGS. 31A-31E present embodiments of a record processing and storagesystem 2505 that generates pages 2515 via a plurality of processing coreresources 48. The plurality of processing core resources 48 utilized toimplement page generator 2511 can be utilized to implement the pagegenerator 2511 of FIG. 25A, the page generator 2511 of each streamloader module 2510 of FIG. 25B, the plurality if processing coreresources 48 of the page generator 2511 of FIG. 25C, and/or any otherembodiment of page generator 2511 discussed herein. The plurality ofprocessing core resources 48-1-48-W of page generator 2511 can beimplemented by utilizing some or all processing core resources 48 of oneor more nodes 37 and/or of one or more computing devices 18. Theplurality of processing core resources 48-1-48-W can be implementedutilizing one or more processing core resources 48 of the node 37 ofFIG. 13 . In some cases, the plurality of processing core resources48-1-48-W are each implemented by a single stream loader module 2510. Insome cases, each stream loader module 2510 includes and utilizes its ownsubset of the plurality of processing core resources 48-1-48-W, forexample, where each subset of the plurality of processing core resources48-1-48-W is included in one or more nodes 37 utilized to implement thecorresponding stream loader module 2510. Some or all features and/orfunctionality of embodiments of the record processing and storage system2505 of FIGS. 31A-31E can be utilized to implement the record processingand storage system 2505 of FIG. 25A, FIG. 25B, FIG. 25C, and/or anyother embodiment of record processing and storage system 2505 describedherein.

Individual processing core resources 48 on one or more nodes can each beresponsible for building pages 2515 from batches of received records.For example, processing core resources 48 process batches of records,such as the set of records 2422 included in one or more labeled row data3010 received from one or more data sources, as discussed previously inconjunction with FIG. 25C. Ideally, work is balanced such that allprocessing core resources 48 are maximizing utilization at any giventime without waiting for additional batches to be processed, and withouthaving a large queue of its own batches waiting to be processed whileother processor core resources 48 aren't working.

However, batches of records can vary in size, for example, based ondifferent labeled row data 3010 having different numbers of records,based on different records 2422 having different numbers of columns,and/or based on different records including columns of different sizeddata types. Furthermore, particular processing core resources 48 mayalso be responsible for additional types of tasks, such as generatingsegments from pages and/or performing other functionality performed bythe record processing and storage system 2505 described herein.

For at least these reasons, the processing time required for a givenprocessing core resource 48 to process different batches of records canvary and/or can be unpredictable. Therefore, it can be unideal forlabeled row data 3010 to be assigned to particular processing coreresources 48, as the desired balancing and maximization of utilizationmay not be achieved. In particular, if batches of records were assignedto processing core resource 48 in accordance with a data dependentscheme, for example, where different types of records and/or recordsfrom different data sources are assigned to different processing coreresources 48, then it could be possible for processing core resource 48to be oversubscribed relative to others when data bias exists. Ifbatches of records were assigned to processing core resource 48 in anon-data dependent manner, for example, based on a round robin scheme,based on minimum pending queue length, or another non-data dependentmanner, then the unknown complexity of the total pending queue and/orthe unknowable but ever-changing load on each processing core resource48 itself, means that faster elements could still require beingartificially stalled behind slower ones.

FIGS. 31A-31E present embodiments to more effectively balance and/ormaximize utilization of the processing core resources 48-1-48-W. Batchesof records can be logically placed, in order per stream source, into alogical pool. For example, as illustrated in FIG. 31A, a pending rowdata pool 3410 includes a plurality of labeled row data 3010 receivedfrom one or more data sources that are pending processing into pages2511 by the page generator 2511.

Processing core resources 48 can each extract batches from this pool forprocessing as they finish processing other batches, finish performingother tasks, and/or otherwise have the capacity to process new batches.In this manner, a slow processing core resource 48, such as a processingcore resource 48 currently processing a particularly large batch ofrecords, currently processing a batch of records that takes long time toprocess, and/or currently performing other tasks, doesn't take onadditional work that would otherwise be completable via anotherprocessing core resources 48. This improves the technology of databasesystems by improving the efficiency and utilization of resources duringdata ingress. This further improves the technology of database systemsby increasing the rate at which incoming records can processed intopages 2511, enables greater rates of records to be received andprocessed for storage, allowing for a richer and/or denser databasesystem 10.

Furthermore, processing core resources 48 can each extract batches inaccordance with an ordering of the batches, for example, based on a mostfavorably ordered batch number of the batches in the pending row datapool 3410. This mechanism of retrieving batches for processing preservesthe ordering of the batches, as batches will begin their processing bythe page generator 2511 in accordance with their respective ordering.This mitigates how out-of-order batch processing will be completed. Thispreserving of the batch ordering can further improve the technology ofdatabase systems by reducing the delay in communication of rowconfirmation data 3030 with data sources 2501 based on initiatingprocessing of records in order. This preserving of the batch orderingcan alternatively or additionally further improve the technology ofdatabase systems by improving the efficiency of the deduplicationprocess because: fewer records may require retransmission when delay incommunication of row confirmation data 3030 to data sources isminimized; and/or records requiring deduplication can be received andprocessed within shorter time intervals, which can decrease the numberof pages required for comparison in deduplicating each page in someembodiments of the row deduplication module 3050 to improve efficiencyof the row deduplication module 3050.

Each labeled row data 3010 included in the pending row data pool 3410 ofFIGS. 31A-31E can include a plurality of records 2422 and can furtherinclude a corresponding batch number 3412. For example, each labeled rowdata 3010 is implemented as the labeled row data 3010 discussed inconjunction with FIG. 27B, where the corresponding row data 2910includes a plurality of records 2422, and where the batch numberdescribed in conjunction with FIGS. 31A-31E is implemented by the rownumber 3012 assigned to set of records 2422 in the labeled row data3010. As another example, each labeled row data 3010 is implemented asthe labeled row data 3010 discussed in conjunction with FIG. 27D, wherethe batch number described in conjunction with FIGS. 31A-31E isimplemented by the batch number 3412 of the labeled row data 3010 ofFIG. 27D, and where records in the corresponding row data 2910 includetheir own row numbers 3012.

Each labeled row data 3010 of FIGS. 31A-31E can be generated by andreceived from a data source 2501, for example, as discussed inconjunction with FIG. 27A. In such cases, the corresponding batchnumbers can correspond to the corresponding row data 2910, where thebatch number optionally remains the same for row data 2910 that isretransmitted by the data source 2501. This can be ideal to ensure thatretransmitted data is prioritized and/or processed before other datathat may have been transmitted before this retransmitted data and/orreceived by the record processing and storage system 2505 before thisretransmitted data.

In other embodiments, the labeled row data 3010 of FIGS. 31A-31E canalternatively be generated by the record processing and storage system2505 from one or more row data 2910 of one or more labeled row data 3010received, for example, in succession and/or within a same time interval,from a same data source 2501 and/or from multiple different datasources. In such cases, the batch number 3412 of the labeled row data3010 of FIGS. 31A-31E can optionally be generated by the recordprocessing and storage system 2505, for example, in accordance with anordering that the corresponding one or more corresponding row data 2910included in this labeled row data 3010 was received. This can be idealin maintaining an ordering of batches received across different datasources 2501 because the row numbers 3012 and/or batch numbers 3412generated by each data source may not be correlated.

The batch number 3412 for each labeled row data 3010 of FIGS. 31A-31Ecan be generated by the data source and/or by the record processing andstorage system 2505 in accordance with an ordering scheme, such as thestrictly increasing ordering scheme discussed in conjunction with theexamples of FIGS. 27A-27H. In cases where the batch number 3412 for eachlabeled row data 3010 of FIGS. 31A-31E is generated by the recordprocessing and storage system 2505, the ordering scheme can be afunction of when the corresponding row data is received by the recordprocessing and storage system 2505, for example, where each subsequentlyreceived row data 2910 is assigned a strictly increasing batch number3412 from all previously received row data 2910. In some cases,subsequently received row data 2910 is receives from different ones of aset of data sources 2501 over time, and each subsequently received rowdata 2910 is assigned a strictly increasing batch number 3412 from allpreviously received row data 2910, regardless of which one of aplurality of data sources 2501 sent the corresponding row data 2910.

The pending row data pool 3410 can be implemented via one or more memoryresources accessible by the page generator 2511. In some cases, thepending row data pool 3410 is implemented utilizing the queue reader2559 of FIG. 25B, where the pending row data pool 3410 is optionallyshared and/or accessible by each page generator 2511 of each streamloader module 2510 of FIG. 25B. The pending row data pool 3410 can beimplemented as a sorted list of labeled row data 3010 by batch number3412. For example, the confirmation-pending row list 3020 can beimplemented as and/or based on a queue and/or priority queue that ispopulated with labeled row data 3010 as it is received. The ordering ofthe labeled row data 3010 is in accordance with the ordering schemeutilized to generate the batch numbers 3412. For example, a highestpriority labeled row data 3010 and/or first labeled row data 3010 in thepending row data pool 3410 at a given time corresponds to the labeledrow data 3010 with the most favorably ordered batch number 3412, such asa lowest batch number 3412 in embodiments where batch number 3412 aregenerated as strictly increasing values. The pending row data pool 3410can otherwise have an ordering of labeled row data 3010 by the order inwhich the labeled row data 3010 was generated by a corresponding datasource 2501 and/or by the order in which the labeled row data 3010 wasgenerated and/or received by the record processing and storage system2505.

Each processing core resource 48 can independently retrieve and processlabeled row data 3010 from the pending row data pool 3410 over time, forexample, in parallel with the processing of other labeled row data 3010by other processing core resources 48 and/or without coordination withthe other processing core resources 48. In particular, each processingcore resource 48 can retrieve and/or access the labeled row data 3010 amost favorably ordered batch number 3412 for processing in response tocompleting processing of a previously retrieved labeled row data 3010and/or in response to otherwise becoming available to process a newlabeled row data 3010.

As described herein, each retrieval and processing of a given labeledrow data 3010 from pending row data pool 3410 can be performed by aprocessing core resource 48 within a particular time slice, where theprocessing core resource 48 retrieves and processes a plurality oflabeled row data 3010 over time within a plurality of sequential timeslices. Different time slices can be varying lengths of time based onhow long it takes to process the given labeled row data 3010. Note thatin certain time slices of the plurality of sequential time slices, thecorresponding processing core resource 48 can be instead performingother tasks, such as participating in generation of segments from pagesto implement some or all functionality of segment generator 2517. Overtime, each other processing core resource 48 retrieves and processesother labeled row data 3010 from pending row data pool 3410 and/orperform other tasks in their own pluralities of sequential time slices,which can be overlapping and/or can have non-synchronized time slices ofsame or different lengths due to each processing core resource 48independently processing their retrieved labeled row data 3010 withoutcoordination. Over time, each processing core resource 48 retrieves andprocessing their own, distinct subset of a plurality of labeled row data3010 that was included in the pending row data pool 3410 to generatetheir own distinct subset of pages in a plurality of pages generatedacross all processing core resource 48-1-48-W.

The processing of each given labeled row data 3010 by a singleprocessing core resource 48 can include: generating a portion of a page2515 from the row data 2910 in the given labeled row data 3010;generating one or more entire pages 2515 from the row data 2910 in thegiven labeled row data 3010; generating page metadata 3115 for a page2515 generated from the given labeled row data 3010 based on the batchnumber 3412, one or more row numbers 3012, and/or one or more datasource identifiers 3014 included in the given labeled row data 3010, forexample, as discussed in conjunction with FIG. 30A; performing some ofall of the deduplication of a page 2515 generated from the given labeledrow data 3010 as discussed in conjunction with FIGS. 28A-28D and/or29A-29B; generating and/or facilitating sending of row confirmation data3030 based on the batch number 3412, one or more row numbers 3012,and/or one or more data source identifiers 3014 included in the givenlabeled row data 3010, for example, as discussed in conjunction withFIGS. 27A-27F; generating index data 2516 for a page 2515 generated fromthe given labeled row data 3010; facilitating storage of a page 2515generated from the given labeled row data 3010 in page cache 2512, oneor more page storage 2546, and/or other memory of page storage system2506; performing compression of data values for one or more recordsincluded in row data 2910 of the given labeled row data 3010; performingvalidation of one or more records included in row data 2910 of the givenlabeled row data 3010; and/or performing other processing of labeled rowdata 3010 for generation and/or storage of corresponding pages 2515.

While not depicted in FIG. 31A, the pending row data pool 3410 canoptionally include separate ordered queues and/or otherwise separatepools of data for each corresponding data source identifier 3014 basedon their corresponding batch numbers 3412. In such cases, processingcore resources can extract the most favorably ordered labeled row data3010 of a selected data source identifier 3014. The data sourceidentifier 3014 can be selected by a processing core resources based onbeing assigned to the processing core resource 48, based on theprocessing core resource determining data source identifiers 3014 foreach retrieval in accordance with a round robin approach, based on theprocessing core resource determining a data source identifiers 3014 witha largest corresponding queue and/or a least-recently received mostfavorably ordered labeled row data 3010, and/or based on anotherdetermination. A given processing core resource 48 can retrieve labeledrow data 3010 of the same and/or different data source identifier 3014in sequential ones of its time slices over time based on a determinedordering of the data source identifier 3014 and/or based onindependently determining the data source identifier 3014 of a giventime slice given the current state of the pending row data pool 3410.

FIGS. 31B-31E illustrate an example embodiment of the processing oflabeled row data 3010 by different processing core resources 48-1 ofpage generator 2511 over time. The functionality of page generator 2511as illustrated in the example of FIGS. 31B-31E can be utilized toimplement the page generator 2511 of FIGS. 31A and/or any otherembodiments of page generator 2511 described herein.

FIG. 31B illustrates the state of the page generator 2511 at a firsttime t₁. Processing core resource 48-1 is currently processing labeledrow data 3010-90 that was previously retrieved from pending row datapool 3410 via its own page generator module 3440, as denoted by itsstatus 3445 illustrated in FIG. 31B. Processing core resource 48-W iscurrently processing another labeled row data 3010-85 that waspreviously retrieved from pending row data pool 3410 via its own pagegenerator module 3440, as denoted by its status 3445 illustrated in FIG.31B. Each page generator module 3440 can be implemented via processingresources of the corresponding processing core resource 48. Status 3445can be stored and/or determined by the corresponding processing coreresource 48. Alternatively, status 3445 is depicted in FIGS. 31B-31E todenote the current status of the corresponding processing core resource48 for the purpose of illustrating the functionality of page generator2511, and does not correspond to a value or information stored by thecorresponding processing core resource 48.

A queue 3420 of pending row data pool 3410 includes labeled row data3010-100, 3010-105, and 3010-220. The labeled row data 3010 of FIG. 31Bare denoted 3010-85, 3010-90, 3010-100, 3010-105, and 3010-220 based onhaving corresponding batch numbers 3412 with values 85, 90, 100, 105,and 220, respectively. In some cases, these batch numbers 3412 canoptionally correspond to the row numbers 3012 of corresponding labeledrow data 3010 sent by a data source 2501 in the example of FIGS.27F-27H.

The queue is ordered by batch number 3412 in accordance with theordering scheme. In this case, the most favorably ordered batch number3412 corresponds to a lowest batch number 3412. Thus at time t₁, labeledrow data 3010-100 is ordered first in the queue, and will be retrievedby the next available processing core resource 48. Note that labeled rowdata 3010-85 and labeled row data 3010-90 were previously retrievedbased on having more favorable batch numbers than the other batchnumbers in the pending row data pool 3410.

FIG. 31C illustrates the state of the page generator 2511 at a secondtime t₂ that is after time t₁. At time t₂, processing core resource 48-1has finished processing labeled row data 3010-90 via its page generatormodule 3440. For example, a page 2515-90 was generated to includelabeled row data 3010-90 and/or optionally one or more previouslyreceived and processed labeled row data 3010. Based on competingprocessing of labeled row data 3010-90, status 3445 of processing coreresource 48-1 has changed to include retrieving of the next labeled rowdata. Thus, processing core resource 48-1 can generate a row datarequest to pending row data pool 3410 and/or can otherwise access and/orretrieve a most favorably ordered labeled row data 3010 in queue 3420.Labeled row data 3010-100 is therefore retrieved for pending row datapool 3410 processing by processing core resource 48-1 based on being themost favorably ordered labeled row data 3010 in queue 3420 at this time.For example, no other processing core resources 48-2-48-W changed statusbetween time t₁ and time t₂, or otherwise did not become available toprocess new labeled row data between time t₁ and time t₂.

FIG. 31D illustrates the state of the page generator 2511 at a thirdtime t₃ that is after time t₂. At time t₃, processing core resource 48-Whas finished processing labeled row data 3010-85 via its page generatormodule 3440. For example, a page 2515-85 was generated to includelabeled row data 3010-85 and/or optionally one or more previouslyreceived and processed labeled row data 3010. Note that processing coreresource 48-W retrieved labeled row data 3010-85 before processing coreresource 48-1 retrieved labeled row data 3010-90 based on labeled rowdata 3010-85 being more favorably ordered than labeled row data 3010-90,but processing core resource 48-W finished its processing of labeled rowdata 3010-85 after processing core resource 48-1 finished its processingof labeled row data 3010-90. For example, labeled row data 3010-85 mayhave included more records 2422, may have had a greater data size,and/or may have required lengthier and/or complex processing than3010-90. As another example, processing core resource 48-W may have beenprocessing more slowly or less efficiently than processing core resource48-1.

Based on competing processing of labeled row data 3010-95, status 3445of processing core resource 48-W has changed to include retrieving ofthe next labeled row data. Thus, processing core resource 48-W cangenerate a row data request to pending row data pool 3410 and/or canotherwise access and/or retrieve a most favorably ordered labeled rowdata 3010 in queue 3420. Labeled row data 3010-105 is thereforeretrieved for pending row data pool 3410 processing by processing coreresource 48-W based on being the most favorably ordered labeled row data3010 in queue 3420 at this time. For example, no other processing coreresources 48-1-48-W-1 changed status between time t₂ and time t₃, orotherwise did not become available to process new labeled row databetween time t₂ and time t₃.

Note that the queue 3420 was also updated between time t₂ and time t₃ toinclude at least additional labeled row data 3010-250 based oncorresponding additional row data 2910 being received by the recordprocessing and storage system 2505 and thus being added to the pendingrow data pool 3410 between time t₂ and time t₃. This additional labeledrow data, including labeled row data 3010-250, is less favorably orderedthan the labeled row data 3010 that was already in queue 3420 based ontheir batch numbers being less favorably ordered than the batch numbersof all other labeled row data already included in the pending row datapool 3410 prior to time t₂. For example, these batch numbers are lessfavorably ordered based on the corresponding labeled row data 3010having been generated after and/or received after the other labeled rowdata already included in the pending row data pool 3410 prior to timet₂.

In other periods of time, new labeled row data 3010 can be optionallyordered before other labeled row data already included in the queue 3420based on having a more favorably ordered batch number than this labeledrow data already included in the queue 3420. This new labeled row datawill thus be processed before this other labeled row data in suchembodiments. For example, this new labeled row data 3010 has a morefavorably ordered batch number than other labeled row data alreadyincluded in the queue 3420 based on being retransmitted labeled row data3010 with a same, old batch number 3412 due to not being confirmed inrow confirmation data 3030 for its prior one or more transmissions asdiscussed in conjunction with FIG. 27A-27H.

FIG. 31E illustrates the state of the page generator 2511 at a fourthtime t₄ that is after time t₃. At time t₄, processing core resource 48-Whas finished processing labeled row data 3010-105 via its page generatormodule 3440. For example, a page 2515-105 was generated and/or partiallygenerated to include labeled row data 3010-105. Based on competingprocessing of labeled row data 3010-105, status 3445 of processing coreresource 48-W has changed to include retrieving of the next labeled rowdata. Thus, processing core resource 48-W can generate a row datarequest to pending row data pool 3410 and/or can otherwise access and/orretrieve a most favorably ordered labeled row data 3010 in queue 3420.Labeled row data 3010-220 is therefore retrieved for pending row datapool 3410 processing by processing core resource 48-W based on being themost favorably ordered labeled row data 3010 in queue 3420 at this time.For example, other labeled row data 3010 included between labeled rowdata 3010-105 and labeled row data 3010-220 in the ordering of the queue3420 at time t₃ were each retrieved by other ones of the processing coreresource 48 of the page generator 2511 between time t₃ and t₄ based onbeing more favorably ordered than 3010-220 and based on these otherprocessing core resources 48 having become available to process labeledrow data. However, in this example, processing core resource 48-1 hasnot retrieved any other labeled row data 3010 for processing betweentime t₃ and t₄ based on still processing labeled row data 3010-100 attime t₄.

Additional labeled row data 3010 can be received and added to thepending data pool over 3410 time, and can each be similarly processed bya processing core resource 48 based on becoming available. Each labeledrow data 3010 of a plurality of labeled row data in pending row datapool 3410 over time can thus have their processing initiated at timesordered in accordance with the ordering dictated by their batch numbers3412. However, differences in processing time for different labeled rowdata 3010 can cause the processing of the each of the plurality oflabeled row data to end in accordance with a different ordering. Thisdifferent ordering over time can be only slightly different from and/orcan be sufficiently similar to the ordering dictated by batch numbers3412, based on the processing being initiated in accordance with theordering dictated by batch numbers 3412.

FIG. 31F illustrates a method for execution by a record processing andstorage system 2505. For example, the database system 10 can utilize atleast one processing module of one or more nodes 37 of one or morecomputing devices 18, where the one or more nodes execute operationalinstructions stored in memory accessible by the one or more nodes, andwhere the execution of the operational instructions causes the one ormore nodes 37 to execute, independently or in conjunction, the steps ofFIG. 31F. As another example, one or more nodes 37 can each utilize aplurality of processing core resources 48, where each of the pluralityof processing core resources 48 of a given node 37 can independentlyperform some or all of the steps of 31F in parallel, withoutcoordination with other ones of the plurality of processing coreresources 48. Some or all of the method of FIG. 31F can be performed bythe page generator 2511 and/or the page storage system 2506 of FIG. 25A.Some or all of the method of FIG. 31F can be performed by one or morestream loader modules 2510 of FIG. 25B, independently or in conjunction.Some or all of the method of FIG. 31F can be performed by the pagegenerator of 2511 of FIGS. 31A-31F, for example, by utilizing theplurality of processing core resources 48-1-48W and/or by utilizing thepending row data pool 3410. Some or all of the steps of FIG. 31F canoptionally be performed by any other processing module of the databasesystem 10. Some or all of the steps of FIG. 31F can optionally beperformed by one or more data sources 2501 and/or can be performed viacommunication with one or more data sources 2501. Some or all of thesteps of FIG. 31F can be performed to implement some or all of thefunctionality of the record processing and storage system 2505 of FIG.25A and/or FIG. 25B. Some or all of the steps of FIG. 31F can beperformed to implement some or all of the functionality of the pagegenerator 2511 of FIGS. 31A-31F. Some or all steps of FIG. 31F can beperformed by database system 10 in accordance with other embodiments ofthe database system 10 and/or nodes 37 discussed herein.

Step 3182 includes receiving a plurality of row data. Each row data caninclude a set of rows. The plurality of row data can be received fromone or more data sources 2501 over time. Step 3184 includes adding eachof the plurality of row data to a pending row data pool in conjunctionwith a corresponding plurality of batch numbers. In some cases, eachbatch number is received with each row data in labeled row datagenerated by a corresponding data source that sent the labeled row data.In such cases, the batch numbers can be generated by each data source2501 in accordance with an ordering of the corresponding row data, forexample, where the batch number is implemented as row number 3012 and/oris based on a row number 3012 included in the corresponding row data.For example, batch numbers can strictly increase for each subsequentlygenerated row data by the corresponding data source. Alternatively, thebatch numbers are assigned to each row number as they are received. Insuch cases, the batch numbers can be generated in accordance with anordering that the corresponding row data is received from one or moredata sources. For example, batch numbers can strictly increase for eachsubsequently received row data. In such cases, batch numbers canoptionally increase across all labeled row data received from multipledata sources based on the ordering of receipt, regardless of data sourcethat sent the data.

Step 3186 includes generating a plurality of pages from the plurality ofrow data via a plurality of processing core resources. Each processingcore resource in the plurality of processing core resources can processa corresponding subset of the plurality of pages, independently fromand/or in parallel with processing of other subsets of the plurality ofpages via other ones of the plurality of processing core resources. Eachprocessing core resource can process its corresponding subset of theplurality of pages over time by performing step 3188 and/or step 3190.

Step 3188 includes via each processing core resource, receiving, in eachtime slice of a plurality of time slices, one row data from the pendingrow data pool at with a most favorably ordered batch number of row datain the pending row data pool at the each time slice. Step 3188 can beperformed based on completing processing of a previously retrieved rowdata in a previous time slice of the plurality of time slices and/orbased on otherwise based on becoming available to process new row data.

Step 3190 includes, via each processing core resource, processing, ineach time slice of a plurality of time slices, the one row data togenerate at least one of the plurality of pages. In some cases eachprocessing resource can further store each page once completinggeneration of each page. Each page can be generated to include a singlerow data and/or to include multiple row data. Pages including multiplerow data can optionally include one or more row data from multipledifferent data sources 2501. In some cases, processing the one row datafurther includes storing the corresponding at least one of the pluralityof pages in a page storage system 2506 and/or writing the one row datato a page storage system 2506 as a portion of or the entirety of acorresponding page.

Steps 3188 and 3190 can be repeated by each processing core resourceover time for each subsequent time slice. Note that the length of timeslices can be different and independent for each of the processing coreresources based on independently processing their retrieved row data indiffering time frames. Different row data can be processed in differentlengths of time based on including different numbers of rows and/ordifferent lengths of data based on having fields with different datatypes and/or fields with variable-length data types.

As an example of one processing core resource repeating steps 3188 and3190, a particular processing core resource can retrieve, in a firsttime slice, one row data from the pending row data pool with a mostfavorably ordered batch number of row data in the pending row data poolat the first time slice. For example, the processing core resourceretrieves the one row data at the first time slice based on completingprocessing of a previously retrieved row data in an immediately previoustime slice. The particular processing core resource can then process theone row data in the first time slice to generate at least a portion ofat least one of the plurality of pages. The particular processing coreresource can then retrieve, at a second time slice after the first timeslice, another row data from the pending row data pool with a mostfavorably ordered batch number of row data in the pending row data poolat the second time. For example, the processing core resource retrievesthe one row data at the first time slice based on completing processingof the one row data, where the second time slice begins immediatelyafter the first time slice elapses as a result of completing processingof the one row data. The particular processing core resource can thenprocess the other row data in the second time slice to generate at leasta portion of the same or different at least one of the plurality ofpages. The one row data and the other row data can be included in thesame pages or can be included in different pages.

This process can repeat for subsequent time slices. Note that one ormore other processing core resources may have retrieved and/or processedother row data after the one row data is retrieved and before the secondrow data is retrieved. These other row data can each have been retrievedby the other processing core resources after the one row data based onhaving batch numbers that are less favorably ordered than the batchnumber of the one row data. These other row data can each have beenretrieved by the other processing core resources before the other rowdata based on having batch numbers that are more favorably ordered thanthe batch number of the other row data.

FIGS. 32A-32B illustrate embodiments of a record processing and storagesystem 2505 that includes a page generator 2511 that implements adurability data generator module 3520. The record processing and storagesystem 2505 of FIGS. 32A-32B can be operable to maintain and/orcommunicate a durability horizon during parallelized processing of rowdata 2910 by page generator 2511, such as the processing of row data2910 over time via the plurality of processing core resources 48-1-48-Was illustrated in FIG. 25C. Some or all features and/or functionality ofembodiments of page generator 2511 of FIGS. 32A-32B can be utilized toimplement the page generator 2511 of FIG. 25A, of FIG. 25B, and/or anyother embodiment of page generator 2511 described herein. Some or allfeatures and/or functionality of embodiments of record processing andstorage system 2505 of FIGS. 32A-32B can be utilized to implement therecord processing and storage system 2505 of FIG. 25A, of FIG. 25B,and/or any other embodiment of record processing and storage system 2505described herein.

At any given point in time, there is a record in a given record streamthat is considered durable, for example, based on being durably storedby the record processing and storage system 2505 in a page 2515 inaccordance with requirements for durable storage discussed previously.Alternatively or in addition, the record is considered durable based on:being received by the record processing and storage system 2505; beingincluded in a page 2515 generated by the record processing and storagesystem 2505, deduplicated by the record processing and storage system2505, and/or stored by the record processing and storage system 2505;and/or being confirmed by the record processing and storage system 2505,for example, as discussed in conjunction with FIG. 27A. This record ispart of some batch of records in some labeled row data 3010 processed bythe page generator 2511, and can represent a “durability horizon” forthe corresponding record stream.

A guaranteed durability horizon can be determined for each data sourceas their batches are processed in parallel by different processing coreresources 48-1-48-W over time. This guaranteed durability horizon cancorrespond to a baseline durability value that can be computed via asimple integer comparison. The guaranteed durability horizon for anygiven data source 2501 can be maintained across all processing coreresources 48 based on evaluating the minimum known horizon for allprocessing core resources 48 that is not greater than a known pendingvalue. For example, this baseline durability value can indicate aparticular row number of a particular corresponding record and/or ofparticular corresponding row data 2910 of this guaranteed durabilityhorizon. A guaranteed durability horizon can be maintained for each datasource 2501-1-2501-L.

This guaranteed durability horizon can be indicated as row durabilitydata 3530 generated by a durability data generator module 3520 of FIG.32A. This row durability data 3530 is generated by and/or maintained bythe durability data generator module 3520 based on row numbers 3012whose corresponding records 2422 and/or corresponding row data 2910 wereprocessed by a processing core resource 48. For example, these rownumbers 3012 are determined based on being indicated in and/or extractedfrom the labeled row data 3010 processed by a processing core resource48. In particular, the corresponding page generator 2511 of FIG. 32A canbe implemented to perform some or all functionality of the pagegenerator 2511 of FIGS. 31A-31E, and the computing of this guaranteeddurability horizon can leverage the in-order initiating of processing oflabeled row data 3010 by the plurality of processing core resources48-1-48-W as described in conjunction with FIGS. 31A-31E to determineand/or communicate a conservative, baseline durability value of rowdurability data 3530. The computing of this guaranteed durabilityhorizon can further leverage the ordering scheme utilized by datasources 2501 to generate row numbers 3012 as discussed in conjunctionwith FIGS. 27A-27H.

These row numbers 3012 can indicate corresponding row data 2910. Theserow numbers 3012 can be implemented as the row numbers 3012 of labeledrow data 3010 indicating a single record 2422, for example, implementedas the row numbers 3012 in embodiments of labeled row data 3010 of FIG.27B and/or 27D. These row numbers 3012 can alternatively or additionallyimplemented as the row numbers 3012 of labeled row data 3010 indicatinga set of multiple records 2422, for example, implemented as the rownumbers 3012 in the embodiments of labeled row data 3010 of FIG. 27Cand/or implemented as the batch numbers 3412 in the embodiments oflabeled row data 3010 of FIG. 27D and/or FIGS. 31A-31E.

The row durability data 3530 indicating this guaranteed durabilityhorizon can be sent to a corresponding data source 2501 and/or can besent to another computing device associated with the corresponding datasource 2501. For example, the data source 2501 and/or the associatedcomputing device can display the received row durability data 3530 to auser and/or administrator associated with the data source 2501. Asanother example, the data source 2501 and/or the associated computingdevice can display the received row durability data 3530 to a userand/or administrator associated with the data source 2501.

The row durability data 3530 can indicate that the record, or set ofmultiple records, whose row number 3012 is indicated by a durabilityvalue of row durability data 3530 is durable. The row durability data3530 can further indicate that all other records sent by thecorresponding data source 2501 whose row numbers that are more favorablyordered than the durability value of row durability data 3530 aredurable. For example, the row durability data 3530 can indicate thatthese records with row numbers that are more favorably ordered than thedurability value are all available for access in query executions and/orwill always be accessed and/or represented in future query executions bythe database system 10 as appropriate based on being durably stored bydatabase system 10. In some cases, only a subset of these other recordsof the corresponding data source 2501 whose row numbers were alsoconfirmed in row confirmation data 3030 of FIG. 27A are guaranteed to bedurable, where records with row numbers that are more favorably orderedthan the durability value not guaranteed to be durable if they were notyet indicated in row confirmation data 3030 received from the recordprocessing and storage system 2505.

Alternatively or in addition, the row durability data 3530 can bereceived in conjunction with the row confirmation data 3030 of FIG. 27Aand/or can be included in the row confirmation data 3030 of FIG. 27A.Alternatively or in addition, the row durability data 3530 can beutilized to implement some or all of the row confirmation data 3030 ofFIG. 27A, for example, where records with a row number indicated by adurability value of row durability data 3530 are considered confirmed bythe record processing and storage system 2505; where some or all recordswith a row number more favorably ordered than the durability value ofrow durability data 3530 are considered confirmed by the recordprocessing and storage system 2505; where the confirmation-pending rowlist 3020 is updated based on the durability value of row durabilitydata 3530; and/or where the tracked transmission starting pointindicator is updated based on the durability value of row durabilitydata 3530, for example, to indicate most favorably ordered row data inconfirmation-pending row list 3020 that is also less favorable than thedurability value of row durability data 3530.

Note that because the durability value of row durability data 3530 is aconservatively determined value, some records sent by the correspondingdata source 2501 with corresponding row numbers 3012 that are lessfavorably ordered than the durability value of row durability data 3530may also be durable when the row durability data 3530 is generated.However, the corresponding data source 2501 cannot determine whichadditional records with less favorably ordered row numbers 3012 may bedurable until subsequent row durability data 3530 is received with a newdurability value. For example, these additional records are laterguaranteed to be durable based on being more favorably ordered than thenew durability value in subsequently received row durability data 3530.

FIG. 32B illustrates an example embodiment of the durability datagenerator module 3520 of FIG. 32A. A plurality of durability datamanagers 3522-1-3522-1 can be implemented by the durability datagenerator module 3520 to generate and/or maintain a plurality of rowdurability data 3530-1-3530-L, where each durability data generatormodule 3520 and each row durability data 3530-1-3530-L corresponds toone of the data sources 2501-1-2501-L. For example, row durability data3530 can be separately generated based on data source identifiers 3014associated with each row number 3012 in the labeled row data 3010 and/orotherwise based on the data source 2501 from which the corresponding rowdata 2910 was received.

Each durability data manager 3522 can receive and/or determine processedrow numbers 3012 over time as the corresponding labeled row data 3010 isprocessed by a processing core resources 48. For each processing coreresource 48-1-48-W, one of a corresponding set of row number updatemodules 3521-1-3521-W determined and/or maintains a least favorablyordered processed row number 3523-1-3523-W over time. As illustrated inFIG. 32A, row number update module 3521-1-1 generates a least favorablyordered processed row number 3523-1-1 based on maintaining the leastfavorably ordered processed row number 3523 generated by processing coreresource 48-1 for records associated with data source identifier 3014-1,and row number update module 3521-1-W generates a least favorablyordered processed row number 3523-1-W based on maintaining the leastfavorably ordered processed row number 3523 generated by processing coreresource 48-W for records associated with data source identifier 3014-1.While not depicted, a row number update module 3521-2-3 would similarlygenerate a least favorably ordered processed row number 3523-2-3 basedon maintaining the least favorably ordered processed row number 3523generated by processing core resource 48-3 for records associated withdata source identifier 3014-2. Thus, L×W most-recently processed rownumbers 3523 can be maintained, where each least favorably orderedprocessed row number corresponds to one data source identifier 3014 andfurther corresponds to one processing core resource 48.

In some cases, each processing core resource 48 can optionally implementits own row number update module 3521 as it processes labeled row data3010 to maintain the least favorably ordered processed row number 3523for each data source 2501-1-2501-L. For example, processing coreresource 48-1 implements the set of L row number update modules3521-1-1-3521-L-1.

Each least favorably ordered processed row number 3523 can correspond toa least favorably ordered row number, in accordance with the orderingscheme, for the corresponding data source identifier 3014 whosecorresponding row data 2910 that has been made durable by and/or hasbeen otherwise processed by the corresponding processing core resource48. For example, in embodiments where the ordering scheme corresponds tostrictly increasing valued row numbers, the least favorably orderedprocessed row number 3523 can correspond to a maximum row number thathas been made durable by and/or has been otherwise processed by thecorresponding processing core resource 48. As new row data 2910 is madedurable and/or is processed by a particular processing core resource 48,the least favorably ordered processed row number 3523 for the particularprocessing core resource 48 and for the corresponding data sourceidentifier 3014 can be updated to reflect this the row number 3012 ofthis row data 2910 if this row number 3012 is less favorably orderedthan the current value of the corresponding least favorably orderedprocessed row number 3523.

In some or all cases, the least favorably ordered processed row number3523 can correspond to a most-recently processed row number 3523. Forexample, this can be the case if row numbers 3012 strictly adhere totheir ordering for processing by processing core resource 48 over time,for example, based on being implemented as the batch numbers 3412 ofFIGS. 31A-31E and/or based on processing core resources 48 retrievinglabeled row data 3010 from the pending row data pool 3410 as discussedin conjunction with FIGS. 31A-31E. In such cases, as new row data 2910is made durable and/or is processed by a particular processing coreresource 48, the least favorably ordered processed row number 3523 forthe particular processing core resource 48 and for the correspondingdata source identifier 3014 can be updated to reflect this the rownumber 3012 of this row data 2910, for example, automatically based onbeing most recently processed.

A durability data generator module 3520 of each durability data managercan generate the durability value 3550 of the row durability data forthe corresponding data source identifier 3014. In the example embodimentdepicted in FIG. 32B, the durability value 3550 for the correspondingdata source identifier 3014 at a given time is calculated as the mostfavorably ordered row number of the set of least favorably orderedprocessed row number 3523-1-3523-L for the given data source identifier3014 at the given time. In particular, in cases where the orderingscheme corresponds to strictly increasing row numbers over time, thedurability value 3550 for the corresponding data source identifier 3014at a given time is calculated as the minimum of the set of leastfavorably ordered processed row number 3523-1-3523-L for the given datasource identifier 3014.

In some cases a generate durability value determination module 3532 canbe implemented for each durability data manager 3522-1-3522-L. Thegenerate durability value determination module 3532 can indicate when togenerate a new durability value and/or can trigger the generating of anew durability value as an instruction and/or interrupt. The generatedurability value determination module 3532 can determine when togenerate updated durability values over time for a corresponding datasource 2501 based on durability value generation requirement data.

The durability value generation requirement data can be automaticallygenerated, can be received, can be configured via user input, can beconfigured and/or received by a corresponding data source, can beaccessed from memory, and/or can otherwise be determined by thedurability data generator module. The durability value generationrequirement data can indicate: a schedule for generating durabilityvalues over time; a predetermined time interval for generatingdurability values over time; an instruction to generate a new durabilityvalue as soon as possible; an instruction to generate new durabilityvalues based on one, all, or at least a predetermined number ofprocessing core resources 48-1-48-W having their least favorably orderedprocessed row numbers 3523 for the data source since the last durabilityvalue was updated; and/or based on another determination.

In some cases, row durability data 3530 is generated for different datasources 2501 at different times and/or at different frequencies, forexample, based on the different data sources 2501 configuring orotherwise having different durability value generation requirement data.In other cases, row durability data 3530 is generated for some or alldata sources 2501-1-2501-L at a same time, for example, based on atdifferent times and/or at different frequencies, for example, based onimplementing a common generate durability value determination module3532 across some or all data sources 2501-1-2501-L and/or based on someor all data sources 2501-1-2501-L having same durability valuegeneration requirement data.

FIG. 32C illustrates a method for execution by a record processing andstorage system 2505. For example, the database system 10 can utilize atleast one processing module of one or more nodes 37 of one or morecomputing devices 18, where the one or more nodes execute operationalinstructions stored in memory accessible by the one or more nodes, andwhere the execution of the operational instructions causes the one ormore nodes 37 to execute, independently or in conjunction, the steps ofFIG. 32C. As another example, one or more nodes 37 can each utilize aplurality of processing core resources 48, where each of the pluralityof processing core resources 48 of a given node 37 can independentlyperform some or all of the steps of 32C in parallel, withoutcoordination with other ones of the plurality of processing coreresources 48. Some or all of the method of FIG. 32C can be performed bythe page generator 2511 and/or the page storage system 2506 of FIG. 25A.Some or all of the method of FIG. 32C can be performed by one or morestream loader modules 2510 of FIG. 25B, independently or in conjunction.Some or all of the method of FIG. 32C can be performed by the pagegenerator of 2511 of FIGS. 31A-31F and/or of FIG. 32A, for example, byutilizing the plurality of processing core resources 48-1-48W, byutilizing the pending row data pool 3410, and/or by utilizing thedurability data generator module 3520. Some or all of the method of 32Ccan be performed by the durability data generator module 3520 of FIG.32B, for example, by implementing at least one row number update module3521, by implementing at least one generate durability valuedetermination module 3532, and/or by implementing at least onedurability data generator module 3520. Some or all of the steps of FIG.32C can optionally be performed by any other processing module of thedatabase system 10. Some or all of the steps of FIG. 32C can optionallybe performed by one or more data sources 2501 and/or can be performedvia communication with one or more data sources 2501. Some or all of thesteps of FIG. 32C can be performed to implement some or all of thefunctionality of the record processing and storage system 2505 of FIG.25A and/or FIG. 25B. Some or all of the steps of FIG. 32C can beperformed to implement some or all of the functionality of thedurability data generator module 3520 of FIGS. 32A-32B. Some or allsteps of FIG. 32C can be performed by database system 10 in accordancewith other embodiments of the database system 10 and/or nodes 37discussed herein.

Step 3282 includes generating a plurality of pages from a plurality ofrow data via a plurality of processing core resources. Each processingcore resource in the plurality of processing core resources can processa corresponding subset of the plurality of pages, independently from andin parallel with processing of other subsets of the plurality of pagesvia other ones of the plurality of processing core resources. Performingstep 3282 can include performing of steps 3188 and/or 3190 by each ofthe plurality of processing core resources over time in a plurality oftime slices. Performing step 3282 can include storing the plurality ofpages in a page storage system 2506.

Step 3284 includes determining a set of least favorably ordered rownumbers corresponding to the set of processing core resources based onones of the plurality of row data processed by each of the plurality ofprocessing core resources. For example, if row numbers are strictlyincreasing over time, the least favorably ordered row number for eachprocessing core resource can correspond to the greatest row numberprocessed by each processing core resources. In some cases, the leastfavorably ordered row number for each processing core resource cancorrespond to the least favorably ordered processed row number processedby each processing core resource. In some cases, the least favorablyordered row number for each processing core resource can be based onand/or implemented as a least favorably ordered batch number of FIGS.31A-31F. In some cases, the least favorably ordered row number for eachprocessing core resource can be based on and/or implemented as a rownumber included in the labeled row data generated by and received fromdata sources. The least favorably ordered row number for each processingcore resource can be updated over time as new row data is processed foreach processing core resource. In some cases, the least favorablyordered row number for each processing core resource corresponds only torow data that have been stored in a page storage system 2506 and/orcorresponds to row data that is determined to have been durably storeddue to the processing of the row data by a corresponding processing coreresource.

Step 3286 includes generate row durability data based on identifying amost favorably ordered row number in the set of least favorably orderedrow numbers. For example, if row numbers are strictly increasing overtime, the most favorably ordered row number for each processing coreresource can correspond to the lowest row number processed by eachprocessing core resources. The row durability data can include this mostfavorably ordered row number to indicate that all row data with rownumbers that are more favorably ordered than or equal to this row numberhave been processed into pages and/or are durably stored by the pagestorage system 2506. In some cases, one or more row data with rownumbers that are less favorably ordered than the most favorably orderedrow number in the row durability data, such as one or more least one rowdata with greater row numbers than the row number indicated in the rowdurability data, have also been processed into pages and/or is durablystored by the page storage system 2506.

In some cases, the method further includes determining to generate rowdurability data, where step 3286 is performed based on determining togenerate row durability data. Determining to generate the row durabilitydata can be based on durability value generation requirement data, forexample, that is received, generated via user input, and/or stored inmemory. Determining to generate the row durability data canalternatively or additionally be based on: a predetermined schedule,predetermined intervals, receiving a request from an end user and/or adata source; in response to one or more of the most-recently processedrow numbers being updated since the last row durability data wasgenerated; in response to a predetermined number of the most-recentlyprocessed row numbers being updated since the last row durability datawas generated; in response to all of the most-recently processed rownumbers being updated since the last row durability data was generated;in response to a predetermined number of row data being processed by aparticular processing core resource and/or across all processing coreresources since the since the last row durability data was generated,and/or based on other determined information.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, a set of memory locations within amemory device or a memory section. Such a memory device may be aread-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. The memory device maybe in a form a solid-state memory, a hard drive memory, cloud memory,thumb drive, server memory, computing device memory, and/or otherphysical medium for storing digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a record processing andstorage system, comprising: receiving, via a communication network, arecord stream from a data source that includes a plurality of row data,wherein each of the plurality of row data includes a set of records forstorage as relational database rows of at least one database table of arelational database and further indicates a batch number correspondingto the set of records; adding each of the plurality of row data to apending row data pool; and performing parallelized generation of aplurality of pages from the plurality of row data to each includemultiple records of the plurality of row data via a plurality ofprocessing core resources based on each processing core resource in theplurality of processing core resources processing a corresponding subsetof the plurality of row data, independently from and in parallel withprocessing of other subsets of the plurality of row data via other onesof the plurality of processing core resources, by: retrieving, via theeach processing core resource in each time slice of a plurality of timeslices, one row data from the pending row data pool with a lowest valuedbatch number of row data in the pending row data pool at the each timeslice, based on completing processing of a previously retrieved row datain a previous time slice of the plurality of time slices; andprocessing, via the each processing core resource in the each time sliceof the plurality of time slices, the one row data to participate ingeneration of at least one of the plurality of pages.
 2. The method ofclaim 1, wherein the plurality of time slices have varying durations,and wherein a duration of the each time slice is based on an amount oftime utilized by the each processing core resource to process the onerow data.
 3. The method of claim 2, wherein the varying durations basedon at least one of: different ones of the plurality of row dataincluding different numbers of rows; different ones of the plurality ofrow data including different length of data; different ones of theplurality of row data including data having different fields withdifferent data types; or the plurality of row data includingvariable-length data types.
 4. The method of claim 1, wherein theplurality of time slices corresponds to the each processing coreresource, and wherein the plurality of time slices is asynchronous witheach other plurality of time slices of each other one of the pluralityof processing core resources.
 5. The method of claim 1, wherein theprocessing of the one row data includes at least one of: generating aportion of one page of the at least one of the plurality of pages from acorresponding set of records in the one row data; generating at leastone entire page of the at least one of the plurality of pages from thecorresponding set of records in the one row data; generating pagemetadata for the at least one of the plurality of pages based on the onerow data; facilitating deduplication of the at least one of theplurality of pages based on the one row data; generating rowconfirmation data based on the one row data; generating index data forthe at least one of the plurality of pages based on the one row data; orperforming compression of data values for at least one row included inthe one row data.
 6. The method of claim 1, further comprising:generating a set of segments from the plurality of pages, wherein theset of segments collectively include a plurality of sets of rowsincluded in the plurality of row data in accordance with a column-basedformat; storing the set of segments in at least one memory; andexecuting at least one query again the relational database based onaccessing at least one of the set of segments in the at least onememory.
 7. The method of claim 1, wherein the each processing coreresource generates its own distinct subset of the plurality of pages viaits corresponding subset of the plurality of row data, independentlyfrom and in parallel with generating of other subsets of the pluralityof pages via other ones of the plurality of processing core resources.8. The method of claim 1, wherein a data source generated the batchnumber corresponding to the set of records for the each of the pluralityof row data in accordance with an ordering scheme, wherein the pluralityof row data is received from the data source in a stream of row datatransmitted by the data source in accordance with the ordering scheme,and wherein the lowest valued batch number of the row data in thepending row data pool at the each time slice is determined in accordancewith the ordering scheme.
 9. The method of claim 1, wherein theplurality of row data is received from a plurality of data sources,wherein each of the plurality of row data includes one of a plurality ofdata source identifiers based on a corresponding one of the plurality ofdata sources that generated the each of the plurality of row data, andwherein the one row data is retrieved in the each time slice by the eachprocessing core resource based on having the lowest valued batch numberof a set of row data in the pending row data pool having a particularone of the plurality of data source identifiers.
 10. The method of claim1, wherein the method further comprises, via one processing coreresource in the plurality of processing core resources: retrieving, at afirst time, a first row data from the pending row data pool having afirst batch number based on the first batch number being more favorablyordered than all other batch numbers of all other row data in thepending row data pool at the first time, based on completing processingof a previously retrieved row data; processing the first row data togenerate at least one of the plurality of pages; retrieving, at a secondtime, a second row data from the pending row data pool having a secondbatch number based on the second batch number being more favorablyordered than the all other batch numbers of the all other row data inthe pending row data pool at the second time, based on completingprocessing of the first row data; and processing the second row data togenerate the at least one of the plurality of pages.
 11. The method ofclaim 10, wherein a same page of the at least one of the plurality ofpages is generated to include both the first row data and the second rowdata.
 12. The method of claim 10, wherein the pending row data poolincludes the second row data at the first time, and wherein the secondbatch number of the second row data is less favorable than the firstbatch number of the row data.
 13. The method of claim 10, wherein theone processing core resource receives and processes the first row dataduring a first time slice starting at the first time, wherein the oneprocessing core resource receives and processes the second row dataduring a second time slice starting at the second time, and wherein thesecond time slice begins immediately after the first time slice elapsesas a result of the one processing core resource completing processing ofthe first row data.
 14. The method of claim 10, wherein at the firsttime, a third row data with a third batch number is included in thepending row data pool, wherein the third batch number is less favorablethan the first batch number of the row data, wherein the third batchnumber is more favorable than the second batch number of the second rowdata, and wherein the third row data is not included in the pending rowdata pool at the second time based on a second processing core resourceof the plurality of processing core resources retrieving the third rowdata at a third time that is after the first time and before the secondtime.
 15. The method of claim 14, wherein the one processing coreresource receives and processes the first row data during a first timeslice starting at the first time, wherein the one processing coreresource receives and processes the second row data during a second timeslice starting at the second time, and wherein the method furthercomprises, via the second processing core resource: processing the thirdrow data during a third time slice starting at the third time; whereinthe third time slice overlaps with at least one of: the first time sliceor the second time slice.
 16. The method of claim 15, wherein the thirdtime slice is longer than the first time slice based on at least one of:a size of the third row data being greater than a size of the first rowdata, or the second processing core resource performing less efficientlythan the first one processing core resource.
 17. The method of claim 10,wherein the method further comprises, via the one processing coreresource: participating in a segment generation process to generate aset of segments from a set of pages in the plurality of pages during athird time, wherein the one processing core resource does not retrieverow data from the pending row data pool during the third time.
 18. Themethod of claim 10, wherein the all other row data in the pending rowdata pool at the second time includes at least one new row data notincluded in the all other row data in the pending row data pool at thefirst time based on the new row data being received by the recordprocessing and storage system between the first time and the secondtime.
 19. A record processing and storage system, at least one processorthat includes a plurality of processing core resources; and at least onememory storing a pending row data pool and further storing operationalinstructions that, when executed by the at least one processor, causethe record processing and storage system to: receive, via acommunication network, a record stream from a data source that includesa plurality of row data, wherein each of the plurality of row dataincludes a set of records for storage as relational database rows of atleast one database table and further indicates a batch numbercorresponding to the set of records; add each of the plurality of rowdata to the pending row data pool; and performing parallelizedgeneration of a plurality of pages from the plurality of row data toeach include multiple records of the plurality of row data via theplurality of processing core resources based on each processing coreresource in the plurality of processing core resources processing acorresponding subset of the plurality of row data, independently fromand in parallel with processing of other subsets of the plurality of rowdata via other ones of the plurality of processing core resources, by:retrieving, via the each processing core resource in each time slice ofa plurality of time slices, one row data from the pending row data poolwith a lowest valued batch number of row data in the pending row datapool at the each time slice, based on completing processing of apreviously retrieved row data in a previous time slice of the pluralityof time slices; and processing, via the each processing core resource inthe each time slice of the plurality of time slices, the one row data toparticipate in generation of at least one of the plurality of pages. 20.A non-transitory computer readable storage medium comprises: at leastone memory section that stores operational instructions that, whenexecuted by a processing module that includes a processor and a memory,causes the processing module to: receive, via a communication network, arecord stream from a data source that includes a plurality of row data,wherein each of the plurality of row data includes a set of records forstorage as relational database rows of at least one database table andfurther indicates a batch number corresponding to the set of records;add each of the plurality of row data to a pending row data pool; andperforming parallelized generation of a plurality of pages from theplurality of row data to each include multiple records of the pluralityof row data via a plurality of processing core resources based on eachprocessing core resource in the plurality of processing core resourcesprocessing a corresponding subset of the plurality of row data,independently from and in parallel with processing of other subsets ofthe plurality of row data via other ones of the plurality of processingcore resources, by: retrieving, via the each processing core resource ineach time slice of a plurality of time slices, one row data from thepending row data pool with a lowest valued batch number of row data inthe pending row data pool at the each time slice, based on completingprocessing of a previously retrieved row data in a previous time sliceof the plurality of time slices; and processing, via the each processingcore resource in the each time slice of the plurality of time slices,the one row data to participate in generation of at least one of theplurality of pages.